diff --git a/.eslintrc.json b/.eslintrc.json
index dbcd90ea..9005ee77 100644
--- a/.eslintrc.json
+++ b/.eslintrc.json
@@ -61,6 +61,7 @@
     "node/no-unpublished-import": "off",
     "node/no-unpublished-require": "off",
     "node/no-unsupported-features/es-syntax": "off",
+    "no-lonely-if": "off",
     "node/shebang": "off",
     "object-curly-newline": "off",
     "prefer-destructuring": "off",
diff --git a/CHANGELOG.md b/CHANGELOG.md
index c7f8b00c..d8b417b0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,9 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
 
 ## Changelog
 
+### **HEAD -> main** 2021/04/13 mandic00@live.com
+
+
 ### **1.5.1** 2021/04/13 mandic00@live.com
 
 - fix for safari imagebitmap
diff --git a/assets/tfjs-backend-wasm-simd.wasm b/assets/tfjs-backend-wasm-simd.wasm
index 98697f4b..fecbd55a 100644
Binary files a/assets/tfjs-backend-wasm-simd.wasm and b/assets/tfjs-backend-wasm-simd.wasm differ
diff --git a/assets/tfjs-backend-wasm-threaded-simd.wasm b/assets/tfjs-backend-wasm-threaded-simd.wasm
index 8014bc9d..bb58d77f 100644
Binary files a/assets/tfjs-backend-wasm-threaded-simd.wasm and b/assets/tfjs-backend-wasm-threaded-simd.wasm differ
diff --git a/assets/tfjs-backend-wasm.wasm b/assets/tfjs-backend-wasm.wasm
index 143f229f..6811481c 100644
Binary files a/assets/tfjs-backend-wasm.wasm and b/assets/tfjs-backend-wasm.wasm differ
diff --git a/demo/helpers/webrtc.js b/demo/helpers/webrtc.js
index af99fbbb..56ccabd8 100644
--- a/demo/helpers/webrtc.js
+++ b/demo/helpers/webrtc.js
@@ -41,7 +41,6 @@ async function webRTC(server, streamName, elementName) {
   connection.ontrack = (event) => {
     stream.addTrack(event.track);
     const video = (typeof elementName === 'string') ? document.getElementById(elementName) : elementName;
-    // @ts-ignore
     if (video instanceof HTMLVideoElement) video.srcObject = stream;
     else log('element is not a video element:', elementName);
     // video.onloadeddata = async () => log('resolution:', video.videoWidth, video.videoHeight);
diff --git a/demo/index.js b/demo/index.js
index 45da4889..516014c8 100644
--- a/demo/index.js
+++ b/demo/index.js
@@ -198,7 +198,7 @@ async function setupCamera() {
   ui.busy = true;
   const viewportScale = Math.min(1, Math.round(100 * window.outerWidth / 700) / 100);
   // log('demo viewport scale:', viewportScale);
-  document.querySelector('meta[name=viewport]').setAttribute('content', `width=device-width, shrink-to-fit=no; initial-scale=${viewportScale}`);
+  document.querySelector('meta[name=viewport]').setAttribute('content', `width=device-width, shrink-to-fit=no, initial-scale=${viewportScale}`);
   const video = document.getElementById('video');
   const canvas = document.getElementById('canvas');
   const output = document.getElementById('log');
@@ -587,7 +587,7 @@ async function main() {
     log('overriding worker:', ui.useWorker);
   }
   if (params.has('backend')) {
-    userConfig.backend = JSON.parse(params.get('backend'));
+    userConfig.backend = params.get('backend');
     log('overriding backend:', userConfig.backend);
   }
   if (params.has('preload')) {
diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js
index fc25faac..01d4827c 100644
--- a/dist/human.esm-nobundle.js
+++ b/dist/human.esm-nobundle.js
@@ -5,7 +5,7 @@
   author: <https://github.com/vladmandic>'
   */
 
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X0=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],F0=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],q0=[33,133,362,263,1,78,308],ir=X0.map(e=>pe[e]),ar=F0.map(e=>pe[e]),xr=q0.map(e=>pe[e]);var Et=G.leftEyeLower0,Rt=G.rightEyeLower0,xe={leftBounds:[Et[0],Et[Et.length-1]],rightBounds:[Rt[0],Rt[Rt.length-1]]},qe={count:468,mouth:13,symmetryLine:[13,G.midwayBetweenEyes[0]]},N1={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ye={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Be(e,t,n,A){for(let _=0;_<zt.length;_++){let{key:c,indices:r}=zt[_],s=G[`${n}${c}`];if(!A||A.includes(c))for(let h=0;h<r.length;h++){let a=r[h];e[s[h]]=[t[a][0],t[a][1],(t[a][2]+e[s[h]][2])/2]}}}var jt=class{constructor(t,n,A){var _,c;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=A,this.boxSize=((_=t==null?void 0:t.model)==null?void 0:_.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((c=t==null?void 0:t.model)==null?void 0:c.inputs[0].shape[2]),this.irisSize=(A==null?void 0:A.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,A,_){let c=me({startPoint:n.startPoint,endPoint:n.endPoint}),r=t.map(i=>[c[0]/this.meshSize*(i[0]-this.meshSize/2),c[1]/this.meshSize*(i[1]-this.meshSize/2),i[2]]),s=A!==0?Fe(A,[0,0]):Xe,h=A!==0?r.map(i=>[...k1(i,s),i[2]]):r,a=A!==0?W1(_):Xe,x=[...ie({startPoint:n.startPoint,endPoint:n.endPoint}),1];return h.map(i=>[i[0]+ee(x,a[0]),i[1]+ee(x,a[1]),i[2]])}getLeftToRightEyeDepthDifference(t){let n=t[xe.leftBounds[0]][2],A=t[xe.rightBounds[0]][2];return n-A}getEyeBox(t,n,A,_,c=!1){let r=Ve(He(this.calculateLandmarksBoundingBox([t[A],t[_]]),this.irisEnlarge)),s=me(r),h=o.image.cropAndResize(n,[[r.startPoint[1]/this.meshSize,r.startPoint[0]/this.meshSize,r.endPoint[1]/this.meshSize,r.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return c&&o.ENV.flags.IS_BROWSER&&(h=o.image.flipLeftRight(h)),{box:r,boxSize:s,crop:h}}getEyeCoords(t,n,A,_=!1){let c=[];for(let r=0;r<ye.numCoordinates;r++){let s=t[r*3],h=t[r*3+1],a=t[r*3+2];c.push([(_?1-s/this.irisSize:s/this.irisSize)*A[0]+n.startPoint[0],h/this.irisSize*A[1]+n.startPoint[1],a])}return{rawCoords:c,iris:c.slice(ye.index)}}getAdjustedIrisCoords(t,n,A){let _=t[G[`${A}EyeUpper0`][ye.upperCenter]][2],c=t[G[`${A}EyeLower0`][ye.lowerCenter]][2],r=(_+c)/2;return n.map((s,h)=>{let a=r;return h===2?a=_:h===4&&(a=c),[s[0],s[1],a]})}async predict(t,n){let A=!1,_;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(_=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||_&&_.boxes&&(!n.face.mesh.enabled||_.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let r of _.boxes)this.storedBoxes.push({startPoint:r.box.startPoint.dataSync(),endPoint:r.box.endPoint.dataSync(),landmarks:r.landmarks,confidence:r.confidence});this.storedBoxes.length>0&&(A=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),A){if(!_||!_.boxes||_.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let r=0;r<this.storedBoxes.length;r++){let s=R1({startPoint:this.storedBoxes[r].startPoint,endPoint:this.storedBoxes[r].endPoint},_.scaleFactor),h=He(s),a=Ve(h),x=this.storedBoxes[r].landmarks.arraySync(),i=this.storedBoxes[r].confidence;this.storedBoxes[r]={...a,confidence:i,landmarks:x}}}_&&_.boxes&&_.boxes.forEach(r=>{r.box.startPoint.dispose(),r.box.endPoint.dispose(),r.landmarks.dispose()});let c=o.tidy(()=>this.storedBoxes.map((r,s)=>{let h=r.confidence,a,x=0,i;if(n.face.detector.rotation&&n.face.mesh.enabled&&o.ENV.flags.IS_BROWSER){let[g,d]=r.landmarks.length>=qe.count?qe.symmetryLine:N1.symmetryLine;x=vt(r.landmarks[g],r.landmarks[d]);let T=ie({startPoint:r.startPoint,endPoint:r.endPoint}),v=[T[0]/t.shape[2],T[1]/t.shape[1]],R=o.image.rotateWithOffset(t,x,0,v);i=Fe(-x,T),n.face.mesh.enabled?a=ae({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255):a=ae({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{i=Xe;let g=t.clone();n.face.mesh.enabled?a=ae({startPoint:r.startPoint,endPoint:r.endPoint},g,[this.meshSize,this.meshSize]).div(255):a=ae({startPoint:r.startPoint,endPoint:r.endPoint},g,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:r,faceConfidence:null,boxConfidence:h,confidence:r.confidence,image:a};let[,w,m]=this.meshDetector.predict(a),b=w.dataSync()[0];if(b<n.face.detector.minConfidence)return null;let p=o.reshape(m,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:g,boxSize:d,crop:T}=this.getEyeBox(p,a,xe.leftBounds[0],xe.leftBounds[1],!0),{box:v,boxSize:R,crop:O}=this.getEyeBox(p,a,xe.rightBounds[0],xe.rightBounds[1]),X=this.irisModel.predict(o.concat([T,O])).dataSync(),ot=X.slice(0,ye.numCoordinates*3),{rawCoords:ze,iris:ce}=this.getEyeCoords(ot,g,d,!0),b0=X.slice(ye.numCoordinates*3),{rawCoords:y1,iris:g0}=this.getEyeCoords(b0,v,R),w1=this.getLeftToRightEyeDepthDifference(p);Math.abs(w1)<30?(Be(p,ze,"left",null),Be(p,y1,"right",null)):w1<1?Be(p,ze,"left",["EyeUpper0","EyeLower0"]):Be(p,y1,"right",["EyeUpper0","EyeLower0"]);let T0=this.getAdjustedIrisCoords(p,ce,"left"),P0=this.getAdjustedIrisCoords(p,g0,"right");p=p.concat(T0).concat(P0)}let u=this.transformRawCoords(p,r,x,i);r=He(this.calculateLandmarksBoundingBox(u),1.5);let M=o.tensor2d(u);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&o.ENV.flags.IS_BROWSER){let[g,d]=r.landmarks.length>=qe.count?qe.symmetryLine:N1.symmetryLine;x=vt(r.landmarks[g],r.landmarks[d]);let T=ie({startPoint:r.startPoint,endPoint:r.endPoint}),v=[T[0]/t.shape[2],T[1]/t.shape[1]],R=o.image.rotateWithOffset(t,x,0,v);i=Fe(-x,T),a=ae({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let E={coords:M,box:r,faceConfidence:b,boxConfidence:h,image:a,rawCoords:p},k=Ve(r);return this.storedBoxes[s]={...k,landmarks:u,confidence:r.confidence,faceConfidence:b},E}));return c=c.filter(r=>r!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(r=>r.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(t){let n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c,landmarks:t}}};var Wt=class{constructor(t,n,A,_){this.facePipeline=new jt(t,n,A),this.config=_}async estimateFaces(t,n){let A=await this.facePipeline.predict(t,n),_=[];for(let c of A||[]){if(c.isDisposedInternal)continue;let r=c.coords?c.coords.arraySync():[],s=r.map(i=>[i[0]/t.shape[2],i[1]/t.shape[1],i[2]/this.facePipeline.meshSize]),h={};if(r&&r.length>0)for(let i of Object.keys(G))h[i]=G[i].map(w=>r[w]);let a=c.box?[Math.max(0,c.box.startPoint[0]),Math.max(0,c.box.startPoint[1]),Math.min(t.shape[2],c.box.endPoint[0])-Math.max(0,c.box.startPoint[0]),Math.min(t.shape[1],c.box.endPoint[1])-Math.max(0,c.box.startPoint[1])]:0,x=c.box?[c.box.startPoint[0]/t.shape[2],c.box.startPoint[1]/t.shape[1],(c.box.endPoint[0]-c.box.startPoint[0])/t.shape[2],(c.box.endPoint[1]-c.box.startPoint[1])/t.shape[1]]:[];_.push({confidence:Math.round(100*c.faceConfidence||100*c.boxConfidence||0)/100,boxConfidence:Math.round(100*c.boxConfidence)/100,faceConfidence:Math.round(100*c.faceConfidence)/100,box:a,boxRaw:x,mesh:r,meshRaw:s,annotations:h,image:c.image?c.image.clone():null}),c.coords&&c.coords.dispose(),c.image&&c.image.dispose()}return _}},I=[null,null,null];async function kt(e){return!I[0]&&e.face.enabled||!I[1]&&e.face.mesh.enabled||!I[2]&&e.face.iris.enabled?(I=await Promise.all([!I[0]&&e.face.enabled?E1(e):null,!I[1]&&e.face.mesh.enabled?o.loadGraphModel(S(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!I[2]&&e.face.iris.enabled?o.loadGraphModel(S(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!I[1]||!I[1].modelUrl?f("load model failed:",e.face.mesh.modelPath):e.debug&&f("load model:",I[1].modelUrl)),e.face.iris.enabled&&(!I[2]||!I[1].modelUrl?f("load model failed:",e.face.iris.modelPath):e.debug&&f("load model:",I[2].modelUrl))):e.debug&&(f("cached model:",I[0].model.modelUrl),f("cached model:",I[1].modelUrl),f("cached model:",I[2].modelUrl)),new Wt(I[0],I[1],I[2],e)}var O1=_e,I1=pe;var qt={};J(qt,{PoseNet:()=>Bt,load:()=>Ut});function B0(e){let[t,n,A,_]=e;return{offsets:t,heatmap:n,displacementFwd:A,displacementBwd:_}}var Nt=class{constructor(t){this.model=t}predict(t){return o.tidy(()=>{let A=t.toFloat().div(127.5).sub(1).expandDims(0),c=this.model.predict(A).map(s=>s.squeeze([0])),r=B0(c);return{heatmapScores:r.heatmap.sigmoid(),offsets:r.offsets,displacementFwd:r.displacementFwd,displacementBwd:r.displacementBwd}})}dispose(){this.model.dispose()}};function Ot(e){return Math.floor(e/2)}var It=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Ot(t),t);)this.exchange(t,Ot(t)),t=Ot(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=A}};function U0(e,t,n,A,_,c){let[r,s]=c.shape,h=!0,a=Math.max(n-_,0),x=Math.min(n+_+1,r);for(let i=a;i<x;++i){let w=Math.max(A-_,0),m=Math.min(A+_+1,s);for(let b=w;b<m;++b)if(c.get(i,b,e)>t){h=!1;break}if(!h)break}return h}function L1(e,t,n){let[A,_,c]=n.shape,r=new It(A*_*c,({score:s})=>s);for(let s=0;s<A;++s)for(let h=0;h<_;++h)for(let a=0;a<c;++a){let x=n.get(s,h,a);x<e||U0(a,x,s,h,t,n)&&r.enqueue({score:x,part:{heatmapY:s,heatmapX:h,id:a}})}return r}var we=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],le=we.length,ue=we.reduce((e,t,n)=>(e[t]=n,e),{}),Y0=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Rr=Y0.map(([e,t])=>[ue[e],ue[t]]),Z1=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Lt(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+le)}}function Ue(e,t,n){let{heatmapY:A,heatmapX:_,id:c}=e,{y:r,x:s}=Lt(A,_,c,n);return{x:e.heatmapX*t+s,y:e.heatmapY*t+r}}function Zt(e,t,n){return e<t?t:e>n?n:e}function H1(e,t,n,A){let _=n-e,c=A-t;return _*_+c*c}function Ht(e,t){return{x:e.x+t.x,y:e.y+t.y}}function V1(e,t){let n=t.shape[0],A=new Float32Array(n);for(let _=0;_<n;_++){let c=t.get(_,0),r=t.get(_,1);A[_]=e.get(c,r,_)}return A}function J0(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+le)}}function C0(e,t){let n=[];for(let A=0;A<le;A++){let _=e.get(A,0).valueOf(),c=e.get(A,1).valueOf(),{x:r,y:s}=J0(_,c,A,t);n.push(s),n.push(r)}return o.tensor2d(n,[le,2])}function X1(e,t,n){return o.tidy(()=>e.toTensor().mul(o.scalar(t,"int32")).toFloat().add(C0(e,n)))}function K0(e,t){return o.tidy(()=>{let n=e.div(o.scalar(t,"int32"));return e.sub(n.mul(o.scalar(t,"int32")))})}function F1(e){let[t,n,A]=e.shape;return o.tidy(()=>{let c=e.reshape([t*n,A]).argMax(0),r=c.div(o.scalar(n,"int32")).expandDims(1),s=K0(c,n).expandDims(1);return o.concat([r,s],1)})}var q1=Z1.map(([e,t])=>[ue[e],ue[t]]),Vt=q1.map(([,e])=>e),B1=q1.map(([e])=>e),D0=16;function G0(e,t,n){let A=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,A+e)}}function Xt(e,t,n,A){return{y:Zt(Math.round(e.y/t),0,n-1),x:Zt(Math.round(e.x/t),0,A-1)}}function U1(e,t,n,A,_,c,r,s=2){let[h,a]=A.shape,x=Xt(t.position,c,h,a),i=G0(e,x,r),m=Ht(t.position,i);for(let p=0;p<s;p++){let u=Xt(m,c,h,a),M=Lt(u.y,u.x,n,_);m=Ht({x:u.x*c,y:u.y*c},{x:M.x,y:M.y})}let b=Xt(m,c,h,a),y=A.get(b.y,b.x,n);return{position:m,part:we[n],score:y}}function Y1(e,t,n,A,_,c){let r=t.shape[2],s=Vt.length,h=new Array(r),{part:a,score:x}=e,i=Ue(a,A,n);h[a.id]={score:x,part:we[a.id],position:i};for(let w=s-1;w>=0;--w){let m=Vt[w],b=B1[w];h[m]&&!h[b]&&(h[b]=U1(w,h[m],b,t,n,A,c))}for(let w=0;w<s;++w){let m=B1[w],b=Vt[w];h[m]&&!h[b]&&(h[b]=U1(w,h[m],b,t,n,A,_))}return h}async function J1(e,t,n){let A=0,_=F1(e),c=await Promise.all([e.buffer(),t.buffer(),_.buffer()]),r=c[0],s=c[1],h=c[2],a=X1(h,D0,s),x=await a.buffer(),w=Array.from(V1(r,h)).map((b,y)=>(A+=b,{position:{y:x.get(y,0),x:x.get(y,1)},part:we[y],score:b})),m=w.filter(b=>b.score>n);return _.dispose(),a.dispose(),{keypoints:m,score:A/w.length}}var Q0=1,C1=16;function K1(e,t,{x:n,y:A},_){return e.some(({keypoints:c})=>{let r=c[_].position;return H1(A,n,r.y,r.x)<=t})}function $0(e,t,n){return n.reduce((_,{position:c,score:r},s)=>(K1(e,t,c,s)||(_+=r),_),0)/n.length}function D1(e,t,n,A,_,c,r){let s=[],h=L1(r,Q0,e),a=_^2;for(;s.length<c&&!h.empty();){let x=h.dequeue(),i=Ue(x.part,C1,t);if(K1(s,a,i,x.part.id))continue;let w=Y1(x,e,t,C1,n,A),m=$0(s,a,w);m>r&&s.push({keypoints:w,score:Math.round(100*m)/100})}return s}async function G1(e){return Promise.all(e.map(t=>t.buffer()))}function en(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:A,part:_,position:c})=>({score:A,part:_,position:{x:Math.trunc(c.x*n),y:Math.trunc(c.y*t)}}))}}function Q1(e,[t,n]){let A=e.squeeze(0),_=A.resizeBilinear([t,n]);return A.dispose(),_}function Ft(e,[t,n],[A,_]){return e.map(r=>en(r,t/A,n/_))}var oe;async function tn(e,t,n,A){return new Promise(async _=>{let c=await G1([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),r=c[0],s=c[1],h=c[2],a=c[3],x=await D1(r,s,h,a,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),i=Ft(x,[e.shape[1],e.shape[2]],[A,A]);_(i)})}async function nn(e,t,n,A){return new Promise(async _=>{let c=await J1(t.heatmapScores,t.offsets,n.body.scoreThreshold),r=Ft([c],[e.shape[1],e.shape[2]],[A,A]);_(r)})}var Bt=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let A=Q1(t,[this.inputSize,this.inputSize]),_=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await nn(t,_,n,this.inputSize):await tn(t,_,n,this.inputSize);return _.heatmapScores.dispose(),_.offsets.dispose(),_.displacementFwd.dispose(),_.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function Ut(e){oe?e.debug&&f("cached model:",oe.modelUrl):(oe=await o.loadGraphModel(S(e.modelBasePath,e.body.modelPath)),!oe||!oe.modelUrl?f("load model failed:",e.body.modelPath):e.debug&&f("load model:",oe.modelUrl));let t=new Nt(oe);return new Bt(t)}var Dt={};J(Dt,{HandPose:()=>Qt,load:()=>$t});function Ye(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function be(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function $1(e,t,n){let A=t.shape[1],_=t.shape[2],c=[[e.startPoint[1]/A,e.startPoint[0]/_,e.endPoint[1]/A,e.endPoint[0]/_]];return 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n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c}}};var 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s0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],h0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var Z;async function t1(e){return Z?e.debug&&f("cached model:",Z.modelUrl):(Z=await o.loadGraphModel(S(e.modelBasePath,e.body.modelPath)),Z.width=parseInt(Z.signature.inputs["input_1:0"].tensorShape.dim[2].size),Z.height=parseInt(Z.signature.inputs["input_1:0"].tensorShape.dim[1].size),!Z||!Z.modelUrl?f("load model failed:",e.body.modelPath):e.debug&&f("load model:",Z.modelUrl)),Z}async function n1(e,t){if(!Z||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},A=o.image.resizeBilinear(e,[Z.width,Z.height],!1),_=o.div(A,[255]);A.dispose();let c;if(t.profile){let x=await o.profile(()=>Z.predict(_));c=x.result.find(i=>i.size===195||i.size===155).dataSync(),x.result.forEach(i=>i.dispose()),L("blazepose",x)}else{let x=await Z.predict(_);c=x.find(i=>i.size===195||i.size===155).dataSync(),x.forEach(i=>i.dispose())}_.dispose();let r=[],s=c.length===195?s0:h0,h=5;for(let 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X0=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],F0=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],q0=[33,133,362,263,1,78,308],ir=X0.map(e=>pe[e]),ar=F0.map(e=>pe[e]),xr=q0.map(e=>pe[e]);var Et=G.leftEyeLower0,Rt=G.rightEyeLower0,xe={leftBounds:[Et[0],Et[Et.length-1]],rightBounds:[Rt[0],Rt[Rt.length-1]]},qe={count:468,mouth:13,symmetryLine:[13,G.midwayBetweenEyes[0]]},N1={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ye={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function Be(e,t,n,A){for(let _=0;_<zt.length;_++){let{key:c,indices:r}=zt[_],s=G[`${n}${c}`];if(!A||A.includes(c))for(let h=0;h<r.length;h++){let a=r[h];e[s[h]]=[t[a][0],t[a][1],(t[a][2]+e[s[h]][2])/2]}}}var jt=class{constructor(t,n,A){var _,c;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=A,this.boxSize=((_=t==null?void 0:t.model)==null?void 0:_.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((c=t==null?void 0:t.model)==null?void 0:c.inputs[0].shape[2]),this.irisSize=(A==null?void 0:A.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,A,_){let c=me({startPoint:n.startPoint,endPoint:n.endPoint}),r=t.map(i=>[c[0]/this.meshSize*(i[0]-this.meshSize/2),c[1]/this.meshSize*(i[1]-this.meshSize/2),i[2]]),s=A!==0?Fe(A,[0,0]):Xe,h=A!==0?r.map(i=>[...k1(i,s),i[2]]):r,a=A!==0?W1(_):Xe,x=[...ie({startPoint:n.startPoint,endPoint:n.endPoint}),1];return h.map(i=>[i[0]+ee(x,a[0]),i[1]+ee(x,a[1]),i[2]])}getLeftToRightEyeDepthDifference(t){let n=t[xe.leftBounds[0]][2],A=t[xe.rightBounds[0]][2];return n-A}getEyeBox(t,n,A,_,c=!1){let r=Ve(He(this.calculateLandmarksBoundingBox([t[A],t[_]]),this.irisEnlarge)),s=me(r),h=o.image.cropAndResize(n,[[r.startPoint[1]/this.meshSize,r.startPoint[0]/this.meshSize,r.endPoint[1]/this.meshSize,r.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return c&&o.ENV.flags.IS_BROWSER&&(h=o.image.flipLeftRight(h)),{box:r,boxSize:s,crop:h}}getEyeCoords(t,n,A,_=!1){let c=[];for(let r=0;r<ye.numCoordinates;r++){let s=t[r*3],h=t[r*3+1],a=t[r*3+2];c.push([(_?1-s/this.irisSize:s/this.irisSize)*A[0]+n.startPoint[0],h/this.irisSize*A[1]+n.startPoint[1],a])}return{rawCoords:c,iris:c.slice(ye.index)}}getAdjustedIrisCoords(t,n,A){let _=t[G[`${A}EyeUpper0`][ye.upperCenter]][2],c=t[G[`${A}EyeLower0`][ye.lowerCenter]][2],r=(_+c)/2;return n.map((s,h)=>{let a=r;return h===2?a=_:h===4&&(a=c),[s[0],s[1],a]})}async predict(t,n){let A=!1,_;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(_=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||_&&_.boxes&&(!n.face.mesh.enabled||_.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let r of _.boxes)this.storedBoxes.push({startPoint:r.box.startPoint.dataSync(),endPoint:r.box.endPoint.dataSync(),landmarks:r.landmarks,confidence:r.confidence});this.storedBoxes.length>0&&(A=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),A){if(!_||!_.boxes||_.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let r=0;r<this.storedBoxes.length;r++){let s=R1({startPoint:this.storedBoxes[r].startPoint,endPoint:this.storedBoxes[r].endPoint},_.scaleFactor),h=He(s),a=Ve(h),x=this.storedBoxes[r].landmarks.arraySync(),i=this.storedBoxes[r].confidence;this.storedBoxes[r]={...a,confidence:i,landmarks:x}}}_&&_.boxes&&_.boxes.forEach(r=>{r.box.startPoint.dispose(),r.box.endPoint.dispose(),r.landmarks.dispose()});let c=o.tidy(()=>this.storedBoxes.map((r,s)=>{let h=r.confidence,a,x=0,i;if(n.face.detector.rotation&&n.face.mesh.enabled&&o.ENV.flags.IS_BROWSER){let[g,d]=r.landmarks.length>=qe.count?qe.symmetryLine:N1.symmetryLine;x=vt(r.landmarks[g],r.landmarks[d]);let T=ie({startPoint:r.startPoint,endPoint:r.endPoint}),v=[T[0]/t.shape[2],T[1]/t.shape[1]],R=o.image.rotateWithOffset(t,x,0,v);i=Fe(-x,T),n.face.mesh.enabled?a=ae({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255):a=ae({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{i=Xe;let g=t.clone();n.face.mesh.enabled?a=ae({startPoint:r.startPoint,endPoint:r.endPoint},g,[this.meshSize,this.meshSize]).div(255):a=ae({startPoint:r.startPoint,endPoint:r.endPoint},g,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:r,faceConfidence:null,boxConfidence:h,confidence:r.confidence,image:a};let[,w,m]=this.meshDetector.predict(a),b=w.dataSync()[0];if(b<n.face.detector.minConfidence)return null;let p=o.reshape(m,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:g,boxSize:d,crop:T}=this.getEyeBox(p,a,xe.leftBounds[0],xe.leftBounds[1],!0),{box:v,boxSize:R,crop:O}=this.getEyeBox(p,a,xe.rightBounds[0],xe.rightBounds[1]),X=this.irisModel.predict(o.concat([T,O])).dataSync(),ot=X.slice(0,ye.numCoordinates*3),{rawCoords:ze,iris:ce}=this.getEyeCoords(ot,g,d,!0),b0=X.slice(ye.numCoordinates*3),{rawCoords:y1,iris:g0}=this.getEyeCoords(b0,v,R),w1=this.getLeftToRightEyeDepthDifference(p);Math.abs(w1)<30?(Be(p,ze,"left",null),Be(p,y1,"right",null)):w1<1?Be(p,ze,"left",["EyeUpper0","EyeLower0"]):Be(p,y1,"right",["EyeUpper0","EyeLower0"]);let T0=this.getAdjustedIrisCoords(p,ce,"left"),P0=this.getAdjustedIrisCoords(p,g0,"right");p=p.concat(T0).concat(P0)}let u=this.transformRawCoords(p,r,x,i);r=He(this.calculateLandmarksBoundingBox(u),1.5);let M=o.tensor2d(u);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&o.ENV.flags.IS_BROWSER){let[g,d]=r.landmarks.length>=qe.count?qe.symmetryLine:N1.symmetryLine;x=vt(r.landmarks[g],r.landmarks[d]);let T=ie({startPoint:r.startPoint,endPoint:r.endPoint}),v=[T[0]/t.shape[2],T[1]/t.shape[1]],R=o.image.rotateWithOffset(t.toFloat(),x,0,v);i=Fe(-x,T),a=ae({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let E={coords:M,box:r,faceConfidence:b,boxConfidence:h,image:a,rawCoords:p},k=Ve(r);return this.storedBoxes[s]={...k,landmarks:u,confidence:r.confidence,faceConfidence:b},E}));return c=c.filter(r=>r!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(r=>r.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(t){let n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c,landmarks:t}}};var Wt=class{constructor(t,n,A,_){this.facePipeline=new jt(t,n,A),this.config=_}async estimateFaces(t,n){let A=await this.facePipeline.predict(t,n),_=[];for(let c of A||[]){if(c.isDisposedInternal)continue;let r=c.coords?c.coords.arraySync():[],s=r.map(i=>[i[0]/t.shape[2],i[1]/t.shape[1],i[2]/this.facePipeline.meshSize]),h={};if(r&&r.length>0)for(let i of Object.keys(G))h[i]=G[i].map(w=>r[w]);let a=c.box?[Math.max(0,c.box.startPoint[0]),Math.max(0,c.box.startPoint[1]),Math.min(t.shape[2],c.box.endPoint[0])-Math.max(0,c.box.startPoint[0]),Math.min(t.shape[1],c.box.endPoint[1])-Math.max(0,c.box.startPoint[1])]:0,x=c.box?[c.box.startPoint[0]/t.shape[2],c.box.startPoint[1]/t.shape[1],(c.box.endPoint[0]-c.box.startPoint[0])/t.shape[2],(c.box.endPoint[1]-c.box.startPoint[1])/t.shape[1]]:[];_.push({confidence:Math.round(100*c.faceConfidence||100*c.boxConfidence||0)/100,boxConfidence:Math.round(100*c.boxConfidence)/100,faceConfidence:Math.round(100*c.faceConfidence)/100,box:a,boxRaw:x,mesh:r,meshRaw:s,annotations:h,image:c.image?c.image.clone():null}),c.coords&&c.coords.dispose(),c.image&&c.image.dispose()}return _}},I=[null,null,null];async function kt(e){return!I[0]&&e.face.enabled||!I[1]&&e.face.mesh.enabled||!I[2]&&e.face.iris.enabled?(I=await Promise.all([!I[0]&&e.face.enabled?E1(e):null,!I[1]&&e.face.mesh.enabled?o.loadGraphModel(S(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!I[2]&&e.face.iris.enabled?o.loadGraphModel(S(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!I[1]||!I[1].modelUrl?f("load model failed:",e.face.mesh.modelPath):e.debug&&f("load model:",I[1].modelUrl)),e.face.iris.enabled&&(!I[2]||!I[1].modelUrl?f("load model failed:",e.face.iris.modelPath):e.debug&&f("load model:",I[2].modelUrl))):e.debug&&(f("cached model:",I[0].model.modelUrl),f("cached model:",I[1].modelUrl),f("cached model:",I[2].modelUrl)),new Wt(I[0],I[1],I[2],e)}var O1=_e,I1=pe;var qt={};J(qt,{PoseNet:()=>Bt,load:()=>Ut});function B0(e){let[t,n,A,_]=e;return{offsets:t,heatmap:n,displacementFwd:A,displacementBwd:_}}var Nt=class{constructor(t){this.model=t}predict(t){return o.tidy(()=>{let A=t.toFloat().div(127.5).sub(1).expandDims(0),c=this.model.predict(A).map(s=>s.squeeze([0])),r=B0(c);return{heatmapScores:r.heatmap.sigmoid(),offsets:r.offsets,displacementFwd:r.displacementFwd,displacementBwd:r.displacementBwd}})}dispose(){this.model.dispose()}};function Ot(e){return Math.floor(e/2)}var It=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Ot(t),t);)this.exchange(t,Ot(t)),t=Ot(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=A}};function U0(e,t,n,A,_,c){let[r,s]=c.shape,h=!0,a=Math.max(n-_,0),x=Math.min(n+_+1,r);for(let i=a;i<x;++i){let w=Math.max(A-_,0),m=Math.min(A+_+1,s);for(let b=w;b<m;++b)if(c.get(i,b,e)>t){h=!1;break}if(!h)break}return h}function L1(e,t,n){let[A,_,c]=n.shape,r=new It(A*_*c,({score:s})=>s);for(let s=0;s<A;++s)for(let h=0;h<_;++h)for(let a=0;a<c;++a){let x=n.get(s,h,a);x<e||U0(a,x,s,h,t,n)&&r.enqueue({score:x,part:{heatmapY:s,heatmapX:h,id:a}})}return r}var we=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],le=we.length,ue=we.reduce((e,t,n)=>(e[t]=n,e),{}),Y0=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Rr=Y0.map(([e,t])=>[ue[e],ue[t]]),Z1=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function Lt(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+le)}}function Ue(e,t,n){let{heatmapY:A,heatmapX:_,id:c}=e,{y:r,x:s}=Lt(A,_,c,n);return{x:e.heatmapX*t+s,y:e.heatmapY*t+r}}function Zt(e,t,n){return e<t?t:e>n?n:e}function H1(e,t,n,A){let _=n-e,c=A-t;return _*_+c*c}function Ht(e,t){return{x:e.x+t.x,y:e.y+t.y}}function V1(e,t){let n=t.shape[0],A=new Float32Array(n);for(let _=0;_<n;_++){let c=t.get(_,0),r=t.get(_,1);A[_]=e.get(c,r,_)}return A}function J0(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+le)}}function C0(e,t){let n=[];for(let A=0;A<le;A++){let _=e.get(A,0).valueOf(),c=e.get(A,1).valueOf(),{x:r,y:s}=J0(_,c,A,t);n.push(s),n.push(r)}return o.tensor2d(n,[le,2])}function X1(e,t,n){return o.tidy(()=>e.toTensor().mul(o.scalar(t,"int32")).toFloat().add(C0(e,n)))}function K0(e,t){return o.tidy(()=>{let n=e.div(o.scalar(t,"int32"));return e.sub(n.mul(o.scalar(t,"int32")))})}function F1(e){let[t,n,A]=e.shape;return o.tidy(()=>{let c=e.reshape([t*n,A]).argMax(0),r=c.div(o.scalar(n,"int32")).expandDims(1),s=K0(c,n).expandDims(1);return o.concat([r,s],1)})}var q1=Z1.map(([e,t])=>[ue[e],ue[t]]),Vt=q1.map(([,e])=>e),B1=q1.map(([e])=>e),D0=16;function G0(e,t,n){let A=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,A+e)}}function Xt(e,t,n,A){return{y:Zt(Math.round(e.y/t),0,n-1),x:Zt(Math.round(e.x/t),0,A-1)}}function U1(e,t,n,A,_,c,r,s=2){let[h,a]=A.shape,x=Xt(t.position,c,h,a),i=G0(e,x,r),m=Ht(t.position,i);for(let p=0;p<s;p++){let u=Xt(m,c,h,a),M=Lt(u.y,u.x,n,_);m=Ht({x:u.x*c,y:u.y*c},{x:M.x,y:M.y})}let b=Xt(m,c,h,a),y=A.get(b.y,b.x,n);return{position:m,part:we[n],score:y}}function Y1(e,t,n,A,_,c){let r=t.shape[2],s=Vt.length,h=new Array(r),{part:a,score:x}=e,i=Ue(a,A,n);h[a.id]={score:x,part:we[a.id],position:i};for(let w=s-1;w>=0;--w){let m=Vt[w],b=B1[w];h[m]&&!h[b]&&(h[b]=U1(w,h[m],b,t,n,A,c))}for(let w=0;w<s;++w){let m=B1[w],b=Vt[w];h[m]&&!h[b]&&(h[b]=U1(w,h[m],b,t,n,A,_))}return h}async function J1(e,t,n){let A=0,_=F1(e),c=await Promise.all([e.buffer(),t.buffer(),_.buffer()]),r=c[0],s=c[1],h=c[2],a=X1(h,D0,s),x=await a.buffer(),w=Array.from(V1(r,h)).map((b,y)=>(A+=b,{position:{y:x.get(y,0),x:x.get(y,1)},part:we[y],score:b})),m=w.filter(b=>b.score>n);return _.dispose(),a.dispose(),{keypoints:m,score:A/w.length}}var Q0=1,C1=16;function K1(e,t,{x:n,y:A},_){return e.some(({keypoints:c})=>{let r=c[_].position;return H1(A,n,r.y,r.x)<=t})}function $0(e,t,n){return n.reduce((_,{position:c,score:r},s)=>(K1(e,t,c,s)||(_+=r),_),0)/n.length}function D1(e,t,n,A,_,c,r){let s=[],h=L1(r,Q0,e),a=_^2;for(;s.length<c&&!h.empty();){let x=h.dequeue(),i=Ue(x.part,C1,t);if(K1(s,a,i,x.part.id))continue;let w=Y1(x,e,t,C1,n,A),m=$0(s,a,w);m>r&&s.push({keypoints:w,score:Math.round(100*m)/100})}return s}async function G1(e){return Promise.all(e.map(t=>t.buffer()))}function en(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:A,part:_,position:c})=>({score:A,part:_,position:{x:Math.trunc(c.x*n),y:Math.trunc(c.y*t)}}))}}function Q1(e,[t,n]){let A=e.squeeze(0),_=A.resizeBilinear([t,n]);return A.dispose(),_}function Ft(e,[t,n],[A,_]){return e.map(r=>en(r,t/A,n/_))}var oe;async function tn(e,t,n,A){return new Promise(async _=>{let c=await G1([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),r=c[0],s=c[1],h=c[2],a=c[3],x=await D1(r,s,h,a,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),i=Ft(x,[e.shape[1],e.shape[2]],[A,A]);_(i)})}async function nn(e,t,n,A){return new Promise(async _=>{let c=await J1(t.heatmapScores,t.offsets,n.body.scoreThreshold),r=Ft([c],[e.shape[1],e.shape[2]],[A,A]);_(r)})}var Bt=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let A=Q1(t,[this.inputSize,this.inputSize]),_=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await nn(t,_,n,this.inputSize):await tn(t,_,n,this.inputSize);return _.heatmapScores.dispose(),_.offsets.dispose(),_.displacementFwd.dispose(),_.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function Ut(e){oe?e.debug&&f("cached model:",oe.modelUrl):(oe=await o.loadGraphModel(S(e.modelBasePath,e.body.modelPath)),!oe||!oe.modelUrl?f("load model failed:",e.body.modelPath):e.debug&&f("load model:",oe.modelUrl));let t=new Nt(oe);return new Bt(t)}var Dt={};J(Dt,{HandPose:()=>Qt,load:()=>$t});function Ye(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function be(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function $1(e,t,n){let A=t.shape[1],_=t.shape[2],c=[[e.startPoint[1]/A,e.startPoint[0]/_,e.endPoint[1]/A,e.endPoint[0]/_]];return 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n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c}}};var 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s0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],h0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var Z;async function t1(e){return Z?e.debug&&f("cached model:",Z.modelUrl):(Z=await o.loadGraphModel(S(e.modelBasePath,e.body.modelPath)),Z.width=parseInt(Z.signature.inputs["input_1:0"].tensorShape.dim[2].size),Z.height=parseInt(Z.signature.inputs["input_1:0"].tensorShape.dim[1].size),!Z||!Z.modelUrl?f("load model failed:",e.body.modelPath):e.debug&&f("load model:",Z.modelUrl)),Z}async function n1(e,t){if(!Z||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},A=o.image.resizeBilinear(e,[Z.width,Z.height],!1),_=o.div(A,[255]);A.dispose();let c;if(t.profile){let x=await o.profile(()=>Z.predict(_));c=x.result.find(i=>i.size===195||i.size===155).dataSync(),x.result.forEach(i=>i.dispose()),L("blazepose",x)}else{let x=await Z.predict(_);c=x.find(i=>i.size===195||i.size===155).dataSync(),x.forEach(i=>i.dispose())}_.dispose();let r=[],s=c.length===195?s0:h0,h=5;for(let 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-2Q==`;var u0="1.5.1";var fe,Pe,Me,he,rt,ve,ct,At,_t,dn=class{constructor(t={}){fe.set(this,void 0);Pe.set(this,void 0);Me.set(this,void 0);he.set(this,void 0);this.analyze=(...t)=>{if(!C(this,Pe))return;let n=this.tf.engine().state.numTensors,A=C(this,fe);Ae(this,fe,n);let _=n-A;_!==0&&f(...t,_)};rt.set(this,t=>{if(!C(this,Me))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof o.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});ve.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let A=z();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&f("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&f("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let _=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),c=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&f(`wasm execution: ${_?"SIMD":"no SIMD"} ${c?"multithreaded":"singlethreaded"}`),_||f("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&T1();try{await this.tf.setBackend(this.config.backend)}catch(_){f("error: cannot set backend:",this.config.backend,_)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(f("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let _=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&f(`gl version:${_.getParameter(_.VERSION)} renderer:${_.getParameter(_.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(z()-A)}});ct.set(this,async()=>{let t=(_,c="application/octet-stream")=>fetch(`data:${c};base64,${_}`).then(r=>r.blob()),n,A;switch(this.config.warmup){case"face":n=await t(tt);break;case"full":n=await t(nt);break;default:n=null}if(n){let _=await createImageBitmap(n);A=await this.detect(_,this.config),_.close()}return A});At.set(this,async()=>new Promise(t=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+tt;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+nt;break;default:n=null}let _=new Image;_.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=_.naturalWidth,c.height=_.naturalHeight;let r=c.getContext("2d");r==null||r.drawImage(_,0,0);let s=await this.detect(c,this.config);t(s)},n?_.src=n:t(null)}));_t.set(this,async()=>{var r;if(typeof o.node=="undefined")return this.config.debug&&f("Warmup tfjs-node not loaded"),null;let t=s=>Buffer.from(s,"base64"),n;if(this.config.warmup==="face"&&(n=t(tt)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(nt)),!n)return null;let A=(r=o.node)==null?void 0:r.decodeJpeg(n),_=A.expandDims(0);this.tf.dispose(A);let c=await this.detect(_,this.config);return this.tf.dispose(_),c});this.tf=o,this.draw=a1,this.version=u0,this.config=U(j,t),this.state="idle",Ae(this,fe,0),Ae(this,Pe,!1),Ae(this,Me,!1),Ae(this,he,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>i1(n,this.config),this.classes={facemesh:St,age:st,gender:it,emotion:lt,faceres:bt,body:this.config.body.modelPath.includes("posenet")?qt:e1,hand:Dt,nanodet:A1},this.faceTriangulation=O1,this.faceUVMap=I1,this.sysinfo=f1()}profileData(){return this.config.profile?Ee:{}}similarity(t,n){return this.config.face.description.enabled?Tt(t,n):this.config.face.embedding.enabled?P1(t,n):0}enhance(t){return Pt(t)}match(t,n,A=0){return M1(t,n,A)}async load(t={}){this.state="load";let n=z();t&&(this.config=U(this.config,t)),C(this,he)&&(this.config.debug&&f(`version: ${this.version}`),this.config.debug&&f(`tfjs version: ${this.tf.version_core}`),this.config.debug&&f("platform:",this.sysinfo.platform),this.config.debug&&f("agent:",this.sysinfo.agent),await C(this,ve).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&f("configuration:",this.config),this.config.debug&&f("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?kt(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?ht(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?wt(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?mt(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?pt(this.config):null),this.models.handpose||(this.config.hand.enabled?$t(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?Ut(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?t1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?r1(this.config):null),this.models.nanodet||(this.config.object.enabled?o1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?gt(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await kt(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await ht(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await wt(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await mt(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await pt(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await $t(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await Ut(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await t1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await r1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await o1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await gt(this.config))),C(this,he)&&(this.config.debug&&f("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ae(this,he,!1));let A=Math.trunc(z()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async detect(t,n={}){return new Promise(async A=>{var p,u,M,E;this.state="config";let _;this.config=U(this.config,n),this.state="check";let c=C(this,rt).call(this,t);c&&(f(c,t),A({error:c}));let r=z();await C(this,ve).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let s;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&h1 instanceof ImageBitmap||t instanceof o.Tensor)&&(f("disabling video optimization"),s=this.config.videoOptimized,this.config.videoOptimized=!1),_=z();let h=i1(t,this.config);if(!h||!h.tensor){f("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(z()-_),this.analyze("Get Image:");let a,x,i,w,m;this.config.async?(i=this.config.face.enabled?Mt(this,h.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",_=z(),i=this.config.face.enabled?await Mt(this,h.tensor):[],m=Math.trunc(z()-_),m>0&&(this.perf.face=m)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?a=this.config.body.enabled?(p=this.models.posenet)==null?void 0:p.estimatePoses(h.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?a=this.config.body.enabled?n1(h.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(a=this.config.body.enabled?c1(h.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",_=z(),this.config.body.modelPath.includes("posenet")?a=this.config.body.enabled?await((u=this.models.posenet)==null?void 0:u.estimatePoses(h.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?a=this.config.body.enabled?await n1(h.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(a=this.config.body.enabled?await c1(h.tensor,this.config):[]),m=Math.trunc(z()-_),m>0&&(this.perf.body=m)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(x=this.config.hand.enabled?(M=this.models.handpose)==null?void 0:M.estimateHands(h.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",_=z(),x=this.config.hand.enabled?await((E=this.models.handpose)==null?void 0:E.estimateHands(h.tensor,this.config)):[],m=Math.trunc(z()-_),m>0&&(this.perf.hand=m)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(w=this.config.object.enabled?s1(h.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",_=z(),w=this.config.object.enabled?await s1(h.tensor,this.config):[],m=Math.trunc(z()-_),m>0&&(this.perf.object=m)),this.analyze("End Object:"),this.config.async&&([i,a,x,w]=await Promise.all([i,a,x,w])),o.dispose(h.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let b=[];this.config.gesture.enabled&&(_=z(),b=[...a0(i),...i0(a),...y0(x),...x0(i)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(z()-_)),s&&(this.config.videoOptimized=s),this.perf.total=Math.trunc(z()-r),this.state="idle";let y={face:i,body:a,hand:x,gesture:b,object:w,performance:this.perf,canvas:h.canvas};A(y)})}async warmup(t={}){let n=z();if(t&&(this.config=U(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let A=this.config.videoOptimized;this.config.videoOptimized=!1;let _;typeof createImageBitmap=="function"?_=await C(this,ct).call(this):typeof Image!="undefined"?_=await C(this,At).call(this):_=await C(this,_t).call(this),this.config.videoOptimized=A;let c=z();return this.config.debug&&f("Warmup",this.config.warmup,Math.round(c-n),"ms",_),_}};fe=new WeakMap,Pe=new WeakMap,Me=new WeakMap,he=new WeakMap,rt=new WeakMap,ve=new WeakMap,ct=new WeakMap,At=new WeakMap,_t=new WeakMap;export{dn as Human,dn as default};
+2Q==`;var u0="1.5.1";var fe,Pe,Me,he,rt,ve,ct,At,_t,dn=class{constructor(t={}){fe.set(this,void 0);Pe.set(this,void 0);Me.set(this,void 0);he.set(this,void 0);this.analyze=(...t)=>{if(!C(this,Pe))return;let n=this.tf.engine().state.numTensors,A=C(this,fe);Ae(this,fe,n);let _=n-A;_!==0&&f(...t,_)};rt.set(this,t=>{if(!C(this,Me))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof o.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});ve.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let A=z();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&f("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&f("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let _=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),c=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&f(`wasm execution: ${_?"SIMD":"no SIMD"} ${c?"multithreaded":"singlethreaded"}`),this.config.debug&&!_&&f("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&T1();try{await this.tf.setBackend(this.config.backend)}catch(_){f("error: cannot set backend:",this.config.backend,_)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(f("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let _=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&f(`gl version:${_.getParameter(_.VERSION)} renderer:${_.getParameter(_.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(z()-A)}});ct.set(this,async()=>{let t=(_,c="application/octet-stream")=>fetch(`data:${c};base64,${_}`).then(r=>r.blob()),n,A;switch(this.config.warmup){case"face":n=await t(tt);break;case"full":n=await t(nt);break;default:n=null}if(n){let _=await createImageBitmap(n);A=await this.detect(_,this.config),_.close()}return A});At.set(this,async()=>new Promise(t=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+tt;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+nt;break;default:n=null}let _=new Image;_.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=_.naturalWidth,c.height=_.naturalHeight;let r=c.getContext("2d");r==null||r.drawImage(_,0,0);let s=await this.detect(c,this.config);t(s)},n?_.src=n:t(null)}));_t.set(this,async()=>{let t=_=>Buffer.from(_,"base64"),n;if(this.config.warmup==="face"&&(n=t(tt)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(nt)),!n)return null;let A;if(typeof o.node!="undefined"){let _=o.node.decodeJpeg(n),c=_.expandDims(0);this.tf.dispose(_),A=await this.detect(c,this.config),this.tf.dispose(c)}else this.config.debug&&f("Warmup tfjs-node not loaded");return A});this.tf=o,this.draw=a1,this.version=u0,this.config=U(j,t),this.state="idle",Ae(this,fe,0),Ae(this,Pe,!1),Ae(this,Me,!1),Ae(this,he,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>i1(n,this.config),this.classes={facemesh:St,age:st,gender:it,emotion:lt,faceres:bt,body:this.config.body.modelPath.includes("posenet")?qt:e1,hand:Dt,nanodet:A1},this.faceTriangulation=O1,this.faceUVMap=I1,this.sysinfo=f1()}profileData(){return this.config.profile?Ee:{}}similarity(t,n){return this.config.face.description.enabled?Tt(t,n):this.config.face.embedding.enabled?P1(t,n):0}enhance(t){return Pt(t)}match(t,n,A=0){return M1(t,n,A)}async load(t={}){this.state="load";let n=z();t&&(this.config=U(this.config,t)),C(this,he)&&(this.config.debug&&f(`version: ${this.version}`),this.config.debug&&f(`tfjs version: ${this.tf.version_core}`),this.config.debug&&f("platform:",this.sysinfo.platform),this.config.debug&&f("agent:",this.sysinfo.agent),await C(this,ve).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&f("configuration:",this.config),this.config.debug&&f("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?kt(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?ht(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?wt(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?mt(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?pt(this.config):null),this.models.handpose||(this.config.hand.enabled?$t(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?Ut(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?t1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?r1(this.config):null),this.models.nanodet||(this.config.object.enabled?o1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?gt(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await kt(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await ht(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await wt(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await mt(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await pt(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await $t(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await Ut(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await t1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await r1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await o1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await gt(this.config))),C(this,he)&&(this.config.debug&&f("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),Ae(this,he,!1));let A=Math.trunc(z()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async detect(t,n={}){return new Promise(async A=>{var p,u,M,E;this.state="config";let _;this.config=U(this.config,n),this.state="check";let c=C(this,rt).call(this,t);c&&(f(c,t),A({error:c}));let r=z();await C(this,ve).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let s;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&h1 instanceof ImageBitmap||t instanceof o.Tensor)&&(f("disabling video optimization"),s=this.config.videoOptimized,this.config.videoOptimized=!1),_=z();let h=i1(t,this.config);if(!h||!h.tensor){f("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(z()-_),this.analyze("Get Image:");let a,x,i,w,m;this.config.async?(i=this.config.face.enabled?Mt(this,h.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",_=z(),i=this.config.face.enabled?await Mt(this,h.tensor):[],m=Math.trunc(z()-_),m>0&&(this.perf.face=m)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?a=this.config.body.enabled?(p=this.models.posenet)==null?void 0:p.estimatePoses(h.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?a=this.config.body.enabled?n1(h.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(a=this.config.body.enabled?c1(h.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",_=z(),this.config.body.modelPath.includes("posenet")?a=this.config.body.enabled?await((u=this.models.posenet)==null?void 0:u.estimatePoses(h.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?a=this.config.body.enabled?await n1(h.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(a=this.config.body.enabled?await c1(h.tensor,this.config):[]),m=Math.trunc(z()-_),m>0&&(this.perf.body=m)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(x=this.config.hand.enabled?(M=this.models.handpose)==null?void 0:M.estimateHands(h.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",_=z(),x=this.config.hand.enabled?await((E=this.models.handpose)==null?void 0:E.estimateHands(h.tensor,this.config)):[],m=Math.trunc(z()-_),m>0&&(this.perf.hand=m)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(w=this.config.object.enabled?s1(h.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",_=z(),w=this.config.object.enabled?await s1(h.tensor,this.config):[],m=Math.trunc(z()-_),m>0&&(this.perf.object=m)),this.analyze("End Object:"),this.config.async&&([i,a,x,w]=await Promise.all([i,a,x,w])),o.dispose(h.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let b=[];this.config.gesture.enabled&&(_=z(),b=[...a0(i),...i0(a),...y0(x),...x0(i)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(z()-_)),s&&(this.config.videoOptimized=s),this.perf.total=Math.trunc(z()-r),this.state="idle";let y={face:i,body:a,hand:x,gesture:b,object:w,performance:this.perf,canvas:h.canvas};A(y)})}async warmup(t={}){let n=z();if(t&&(this.config=U(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let A=this.config.videoOptimized;this.config.videoOptimized=!1;let _;typeof createImageBitmap=="function"?_=await C(this,ct).call(this):typeof Image!="undefined"?_=await C(this,At).call(this):_=await C(this,_t).call(this),this.config.videoOptimized=A;let c=z();return this.config.debug&&f("Warmup",this.config.warmup,Math.round(c-n),"ms",_),_}};fe=new WeakMap,Pe=new WeakMap,Me=new WeakMap,he=new WeakMap,rt=new WeakMap,ve=new WeakMap,ct=new WeakMap,At=new WeakMap,_t=new WeakMap;export{dn as Human,dn as default};
 //# sourceMappingURL=human.esm-nobundle.js.map
diff --git a/dist/human.esm-nobundle.js.map b/dist/human.esm-nobundle.js.map
index 5fbc17ec..966fd00b 100644
--- a/dist/human.esm-nobundle.js.map
+++ b/dist/human.esm-nobundle.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/helpers.ts", "../src/sysinfo.ts", "../src/tfjs/tf-browser.ts", "../src/tfjs/backend.ts", "../src/age/age.ts", "../src/profile.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/faceres/faceres.ts", "../src/faceall.ts", "../src/blazeface/facemesh.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/blazeface/facepipeline.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/box.ts", "../src/handpose/handdetector.ts", "../src/handpose/util.ts", "../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/image.ts", "../src/image/imagefx.js", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts", "../src/human.ts"],
-  "sourcesContent": ["// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1250, moduleBytes: 4013323, imports: 7, importBytes: 2255, outputBytes: 2991826, outputFiles: 'dist/tfjs.esm.js' }\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1253, moduleBytes: 4029357, imports: 7, importBytes: 2285, outputBytes: 2998298, outputFiles: 'dist/tfjs.esm.js' }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - compiled\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - sources\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export versions\nexport const version = {\n  tfjs: packageBundle?.version || undefined,\n  'tfjs-core': packageCore?.version || undefined,\n  'tfjs-data': packageData?.version || undefined,\n  'tfjs-layers': packageLayers?.version || undefined,\n  'tfjs-converter': packageConverter?.version || undefined,\n  'tfjs-backend-cpu': version_cpu || undefined,\n  'tfjs-backend-webgl': version_webgl || undefined,\n  'tfjs-backend-wasm': version_wasm || undefined,\n};\n// export const version = {};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore // typescript cannot infer type\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore // typescript cannot infer type\n        this.models.handpose,\n        // @ts-ignore // typescript cannot infer type\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    // @ts-ignore\n    if (typeof tf.node === 'undefined') {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      return null;\n    }\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    // @ts-ignore // tf.node is only defined when compiling for nodejs\n    const data = tf.node?.decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"],
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+  "sourcesContent": ["// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1250, moduleBytes: 4013323, imports: 7, importBytes: 2255, outputBytes: 2991826, outputFiles: 'dist/tfjs.esm.js' }\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1253, moduleBytes: 4029357, imports: 7, importBytes: 2285, outputBytes: 2998298, outputFiles: 'dist/tfjs.esm.js' }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - compiled\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - sources\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export versions\nexport const version = {\n  tfjs: packageBundle?.version || undefined,\n  'tfjs-core': packageCore?.version || undefined,\n  'tfjs-data': packageData?.version || undefined,\n  'tfjs-layers': packageLayers?.version || undefined,\n  'tfjs-converter': packageConverter?.version || undefined,\n  'tfjs-backend-cpu': version_cpu || undefined,\n  'tfjs-backend-webgl': version_webgl || undefined,\n  'tfjs-backend-wasm': version_wasm || undefined,\n};\n// export const version = {};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 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0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input.toFloat(), angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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{\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        this.models.handpose,\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? <any>posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    let res;\n    if (typeof tf['node'] !== 'undefined') {\n      const data = tf['node'].decodeJpeg(img);\n      const expanded = data.expandDims(0);\n      this.tf.dispose(data);\n      // log('Input:', expanded);\n      res = await this.detect(expanded, this.config);\n      this.tf.dispose(expanded);\n    } else {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      /*\n      const input = await canvasJS.loadImage(img);\n      const canvas = canvasJS.createCanvas(input.width, input.height);\n      const ctx = canvas.getContext('2d');\n      ctx.drawImage(img, 0, 0, input.width, input.height);\n      res = await this.detect(input, this.config);\n      */\n    }\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"],
+  "mappings": 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   "names": []
 }
diff --git a/dist/human.esm.js b/dist/human.esm.js
index 7f67198b..ca70e4d1 100644
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@@ -5,31 +5,31 @@
   author: <https://github.com/vladmandic>'
   */
 
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r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Cf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Cf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=ld(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:N}=b;return this.makeTensorFromDataId(_,x,N)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=Cf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=_f(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*mf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Wu||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*mf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of 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+`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":f),d}function Uu(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Dt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Et(e),n!=null){let r=n.length;M(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Hx(t,this.size),this.strides=ro(e)}set(e,...t){t.length===0&&(t=[0]),M(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(a)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Pr().makeTensor(this.values,this.shape,this.dtype)}},Pr=null,ll=null,_I=null;function vI(e){Pr=e}function kI(e){ll=e}function II(e){_I=e}var ze=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Et(e),this.strides=ro(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return ll.buffer(this.shape,this.dtype,e)}bufferSync(){return ll.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ao(this.shape,e,this.dtype==="complex64")}arraySync(){return ao(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Pr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>fd(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Pr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>fd(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Pr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Pr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ll.print(this,e)}clone(){return this.throwIfDisposed(),ll.clone(this)}toString(e=!1){let t=this.dataSync();return bI(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ll.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Pr().makeVariable(this,e,t,n)}};Object.defineProperty(ze,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return Rf("Tensor",()=>ze)}Z();var Hu=class extends ze{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!oa(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Pr().disposeTensor(this),this.dataId=e.dataId,Pr().incRef(this,null)}dispose(){Pr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Hu,Symbol.hasInstance,{value:e=>e instanceof ze&&e.assign!=null&&e.assign instanceof Function});var vr={};Me(vr,{assertTypesMatch:()=>iw,getTensorsInContainer:()=>Df,isTensorInList:()=>SI,makeTypesMatch:()=>vt});var Of;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Of||(Of={}));var zf;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(zf||(zf={}));var Pf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Pf||(Pf={}));var Lf;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Lf||(Lf={}));var Wf;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Wf||(Wf={}));var NI={float32:Lf,int32:zf,bool:Pf,complex64:Wf};function dr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return NI[e][t]}function yd(e){return dr(e,"int32")}function vt(e,t){if(e.dtype===t.dtype)return[e,t];let n=dr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function iw(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function SI(e,t){return t.some(n=>n.id===e.id)}function Df(e){let t=[],n=new Set;return ow(e,t,n),t}function ow(e,t,n){if(e==null)return;if(e instanceof ze){t.push(e);return}if(!TI(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),ow(s,t,n))}}function TI(e){return Array.isArray(e)||typeof e=="object"}function Bf(e){return e.kernelName!=null}var lw=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Gu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new lw}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new AI(this.backendInstance),!0}setupRegisteredKernels(){ol(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){ol(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof wu)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Bf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Bf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let y=dd(p,this.backendName);M(y!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let A=this.backend.numDataIds();o=y.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,A,g);let x=g.map(v=>{if(v.rank!=null)return v;let{dataId:w,shape:b,dtype:k}=v;return this.makeTensorFromDataId(w,b,k)});if(r){let v=this.getTensorsForGradient(p,f,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(y=>this.keep(this.clone(y))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let y=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,y),y}}let{inputs:c,attrs:u}=e,h=Bf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=Ff(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&Ta(e[0])&&(a=e.map(o=>Bu(o)));let s=r.write(a,t,n),i=new ze(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Xx(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new ze(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Hu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Sf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Hu||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Sf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of 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i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),M(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var eb=D({batchNorm4d_:GN});function qN(e,t,n){let r=R(e,"x","bincount"),a=R(t,"weights","bincount");M(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),M(n>=0,()=>`size must be non-negative, but got 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Got stride ${n} and dilation '${s}'`),M(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=ca(d,h,[1,n],r,"NHWC",[1,s],i);return u?H(p,[p.shape[2],p.shape[3]]):H(p,[p.shape[0],p.shape[2],p.shape[3]])}var Id=D({conv1d_:nT});function rT(e,t,n,r,a,s="NHWC",i){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),M(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];M(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),M(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&M(Vt(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=$.runKernel(xs,d,p);return c?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var wm=D({conv2DBackpropInput_:rT});function aT(e,t,n,r,a,s){let i=R(e,"x","conv2dTranspose"),o=R(t,"filter","conv2dTranspose");return wm(n,i,o,r,a,"NHWC",s)}var Sd=D({conv2dTranspose_:aT});function sT(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=R(e,"x","conv3d"),o=R(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),M(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),M(Br(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. 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i=C(e,"boxes","nonMaxSuppression"),o=C(t,"scores","nonMaxSuppression"),l=vl(i,o,n,r,a,null),c=l.maxOutputSize,u=l.iouThreshold,h=l.scoreThreshold,d={boxes:i,scores:o},p={maxOutputSize:c,iouThreshold:u,scoreThreshold:h,padToMaxOutputSize:s},f=D.runKernel($o,d,p);return{selectedIndices:f[0],validOutputs:f[1]}}var NR=O({nonMaxSuppressionPadded_:IR});async function SR(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){let i=C(e,"boxes","nonMaxSuppressionAsync"),o=C(t,"scores","nonMaxSuppressionAsync"),l=vl(i,o,n,r,a,null),c=l.maxOutputSize,u=l.iouThreshold,h=l.scoreThreshold,[d,p]=await Promise.all([i.data(),o.data()]),{selectedIndices:f,validOutputs:m}=bb(d,p,c,u,h,s);return i!==e&&i.dispose(),o!==t&&o.dispose(),{selectedIndices:on(f,"int32"),validOutputs:ge(m,"int32")}}var TR=SR;function ER(e,t,n=!1,r=!1){let a=C(e,"images","resizeBilinear");M(a.rank===3||a.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),M(t.length===2,()=>`Error in resizeBilinear: new 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rM=O({softmaxCrossEntropy_:nM}),aM={fft:ic,ifft:bl,rfft:oc,irfft:Bd},sM={hammingWindow:eR,hannWindow:gb,frame:xb,stft:aR},ze={flipLeftRight:lR,resizeNearestNeighbor:Ib,resizeBilinear:kb,rotateWithOffset:cR,cropAndResize:iR,nonMaxSuppression:dR,nonMaxSuppressionAsync:wR,nonMaxSuppressionWithScore:_R,nonMaxSuppressionWithScoreAsync:kR,nonMaxSuppressionPadded:NR,nonMaxSuppressionPaddedAsync:TR,transform:MR},Sb={bandPart:DR,gramSchmidt:OR,qr:PR},iM={absoluteDifference:BR,computeWeightedLoss:da,cosineDistance:jR,hingeLoss:HR,huberLoss:qR,logLoss:KR,meanSquaredError:YR,sigmoidCrossEntropy:eM,softmaxCrossEntropy:rM},pa=class extends Cw{minimize(e,t=!1,n){let{value:r,grads:a}=this.computeGradients(e,n);if(n!=null){let s=n.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return _e(a),t?r:(r.dispose(),null)}get iterations(){return 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Qd.className="Adamax";Oa(Qd);var lc=class extends pa{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=D.registeredVariables[t];z(()=>{let s=se(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ut(ge(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
-============================`));let r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let r=this.readSync(n.real.dataId),a=this.readSync(n.imag.dataId);return R.mergeRealAndImagArrays(r,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return Lr().makeTensorFromDataId(r,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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n.makeTensorInfo(a.shape,a.dtype,m)}var gD={kernelName:Is,backendName:"cpu",kernelFunc:yD};function xD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=yt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=fr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=bi({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var wD={kernelName:xu,backendName:"cpu",kernelFunc:xD};function bD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=Vm(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var 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a=r.map(o=>n.data.get(o.dataId).values),s=Be(r[0].shape,r[0].dtype),i=s.values;for(let o=0;o<r.length;o++){let l=a[o];for(let c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var u$={kernelName:ds,backendName:"cpu",kernelFunc:l$};function c$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"all");let o=_.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=tr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),f=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let g=A*p,x=m[g];for(let v=0;v<p;++v){let w=m[g+v];x=x&&w}f[A]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let y=n.makeTensorInfo(h,u.dtype,f);if(i){let A=C.expandShapeToKeepDim(h,o),g=mt({inputs:{x:y},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(y),g}return y}var h$={kernelName:lo,backendName:"cpu",kernelFunc:c$};function d$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"any");let o=_.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=tr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),f=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let g=A*p,x=m[g];for(let v=0;v<p;++v){let w=m[g+v];x=x||w}f[A]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let y=n.makeTensorInfo(h,u.dtype,f);if(i){let A=C.expandShapeToKeepDim(h,o),g=mt({inputs:{x:y},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(y),g}return y}var 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U=j-k,X=m.get(y,L,j,A);X>F&&(F=X,a?O=s?((y*r.inHeight+L)*r.inWidth+j)*r.inChannels+A:(L*r.inWidth+j)*r.inChannels+A:O=V*d+U)}}i.set(O,y,g,b,A)}}return i}function T_(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,c=a.dilationDepth,u=a.dilationHeight,h=a.dilationWidth,d=a.effectiveFilterDepth,p=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,y=a.padInfo.top,A=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Be(a.outShape,n),v=x.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],b=a.outShape[2]*a.outShape[3]*a.outShape[4],k=a.outShape[3]*a.outShape[4],N=a.outShape[4];for(let E=0;E<a.batchSize;++E){let F=E*w,O=E*r[0];for(let L=0;L<a.inChannels;++L)for(let V=0;V<a.outDepth;++V){let j=V*i-m,U=j;for(;U<0;)U+=c;let X=Math.min(a.inDepth,d+j),G=F+V*b;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-y,ae=Y;for(;ae<0;)ae+=u;let te=Math.min(a.inHeight,p+Y),ie=G+ee*k;for(let Q=0;Q<a.outWidth;++Q){let he=Q*l-A,oe=he;for(;oe<0;)oe+=h;let fe=Math.min(a.inWidth,f+he),pe=ie+Q*N,ke=g,Se=0,Fe=0;for(let $e=U;$e<X;$e+=c){let et=O+$e*r[1];for(let tt=ae;tt<te;tt+=u){let it=et+tt*r[2];for(let Ke=oe;Ke<fe;Ke+=h){let dt=it+Ke*r[3],je=e[dt+L];if(s==="max"&&je>ke?ke=je:s==="avg"&&(Se+=je,Fe++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Oe=pe+L;v[Oe]=s==="avg"?Se/Fe:ke}}}}return x}function E$(e,t){let n=Be(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let y=0;y<t.inChannels;++y)for(let A=0;A<t.outDepth;++A){let g=A*r-d,x=g;for(;x<0;)x+=i;let v=Math.min(t.inDepth,c+g);for(let w=0;w<t.outHeight;++w){let b=w*a-p,k=b;for(;k<0;)k+=o;let N=Math.min(t.inHeight,u+b);for(let E=0;E<t.outWidth;++E){let F=E*s-f,O=F;for(;O<0;)O+=l;let L=Math.min(t.inWidth,h+F),V=Number.NEGATIVE_INFINITY,j=-1;for(let U=x;U<v;U+=i){let X=U-g;for(let G=k;G<N;G+=o){let ee=G-b;for(let Y=O;Y<L;Y+=l){let ae=Y-F,te=e.get(m,U,G,Y,y);te>=V&&(V=te,j=X*u*h+ee*u+ae)}}}n.set(j,m,A,w,E,y)}}}return n}function C$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ve(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. 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u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,f=u.filterDepth,m=u.filterHeight,y=u.filterWidth,A=u.dilationDepth,g=u.dilationHeight,x=u.dilationWidth,v=u.effectiveFilterDepth,w=u.effectiveFilterHeight,b=u.effectiveFilterWidth,k=v-1-u.padInfo.front,N=b-1-u.padInfo.left,E=w-1-u.padInfo.top,F=Be(s.shape,"float32"),O=1/(f*m*y),L=n.bufferSync(a);for(let V=0;V<u.batchSize;++V)for(let j=0;j<u.inChannels;++j)for(let U=0;U<u.inDepth;++U)for(let X=0;X<u.inHeight;++X)for(let G=0;G<u.inWidth;++G){let ee=U-k,Y=X-E,ae=G-N,te=0;for(let ie=0;ie<v;ie+=A){let Q=(ee+ie)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let he=0;he<w;he+=g){let oe=(Y+he)/d;if(!(oe<0||oe>=u.outHeight||Math.floor(oe)!==oe))for(let fe=0;fe<b;fe+=x){let pe=(ae+fe)/p;pe<0||pe>=u.outWidth||Math.floor(pe)!==pe||(te+=L.get(V,Q,oe,pe,j))}}}F.set(te*O,V,U,X,G,j)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var D$={kernelName:Oh,backendName:"cpu",kernelFunc:$$};function O$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ve([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,y=u.dilationWidth,A=u.effectiveFilterHeight,g=u.effectiveFilterWidth,x=g-1-u.padInfo.left,v=A-1-u.padInfo.top,w=Be(i.shape,"float32"),b=1/(p*f),k=n.data.get(a.dataId).values,N=Be(a.shape,"float32",k);for(let E=0;E<u.batchSize;++E)for(let F=0;F<u.inChannels;++F)for(let O=0;O<u.inHeight;++O)for(let L=0;L<u.inWidth;++L){let V=O-v,j=L-x,U=0;for(let X=0;X<A;X+=m){let G=(V+X)/h;if(!(G<0||G>=u.outHeight||Math.floor(G)!==G))for(let ee=0;ee<g;ee+=y){let Y=(j+ee)/d;Y<0||Y>=u.outWidth||Math.floor(Y)!==Y||(U+=N.get(E,G,Y,F))}}w.set(U*b,E,O,L,F)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var z$={kernelName:Dh,backendName:"cpu",kernelFunc:O$};function P$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;_.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ve([a,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=r;c==null&&(c=.001);let u=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=s?n.data.get(s.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),y=f.length,A=p.length,g=d.length,x=h.length,v=0,w=0,b=0,k=0;for(let N=0;N<u.length;++N)m[N]=f[v++]+(u[N]-h[w++])*p[b++]/Math.sqrt(d[k++]+c),v>=y&&(v=0),w>=x&&(w=0),b>=A&&(b=0),k>=g&&(k=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var L$={kernelName:Ns,backendName:"cpu",kernelFunc:P$};function W$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((A,g)=>A*g),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=mt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=tr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:u}}),y=Ii({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),y}var B$={kernelName:Iu,backendName:"cpu",kernelFunc:W$};function V$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=ty(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var j$={kernelName:zh,backendName:"cpu",kernelFunc:V$},U$=at(Ra,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),H$={kernelName:Ra,backendName:"cpu",kernelFunc:U$},G$=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(_.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let u=o[c],h=l[c];r[c]=Math.hypot(u,h)}return n.makeOutput(r,t.shape,"float32")},q$={kernelName:Su,backendName:"cpu",kernelFunc:G$};function Rl(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var X$={kernelName:Jh,backendName:"cpu",kernelFunc:Rl};function Ml(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(m=>m.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>_.sizeFromShape(m.shape)>0);if(o.length===1)return Gr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(v=>ki({inputs:{input:v},backend:n})),y=o.map(v=>Rl({inputs:{input:v},backend:n})),A=Ml({inputs:m,backend:n,attrs:{axis:s}}),g=Ml({inputs:y,backend:n,attrs:{axis:s}}),x=Bn({inputs:{real:A,imag:g},backend:n});return m.forEach(v=>n.disposeIntermediateTensorInfo(v)),y.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(g),x}let c=o.map(m=>{let y=_.sizeFromShape(m.shape.slice(s));return mt({inputs:{x:m},backend:n,attrs:{shape:[-1,y]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,d=ny(u,i,t[0].dtype,h),p=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return 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F=Math.max(0,Math.ceil((v-E)/p)),O=Math.min(d.outHeight,(d.inHeight+v-E)/p);for(let L=0;L<y;++L){let V=Math.max(0,Math.ceil((x-L)/f)),j=Math.min(d.outWidth,(d.inWidth+x-L)/f);for(let U=0;U<d.inChannels;++U)for(let X=0;X<d.outChannels;++X){let G=0;for(let ee=0;ee<d.batchSize;++ee)for(let Y=F;Y<O;++Y){let ae=E+Y*p-v;for(let te=V;te<j;++te){let ie=L+te*f-x;A?G+=k.get(ee,ae,ie,U)*N.get(ee,Y,te,X):G+=k.get(ee,U,ae,ie)*N.get(ee,X,Y,te)}}g.set(G,E,L,U,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var J$={kernelName:Lh,backendName:"cpu",kernelFunc:Y$};function Q$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r;ve([a,s],"conv2dBackpropInput");let h=_.computeStrides(s.shape),d=_.computeStrides(a.shape),p=C.convertConv2DDataFormat(c),f=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),m=new 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eD={kernelName:xs,backendName:"cpu",kernelFunc:Q$};function tD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;ve([a,s],"conv3d");let c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:y}=c,A=y.front,g=y.left,x=y.top,v=new Dt(c.outShape,a.dtype),w=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,k=v.values,N=_.computeStrides(a.shape),E=_.computeStrides(s.shape);for(let F=0;F<c.batchSize;++F){let O=F*N[0],L=F*v.strides[0];for(let V=0;V<c.outDepth;++V){let j=L+V*v.strides[1],U=V*c.strideDepth-A;for(let X=0;X<u;++X){let G=U+X*p;if(G<0||G>=c.inDepth)continue;let ee=X*E[0],Y=O+G*N[1];for(let ae=0;ae<c.outHeight;++ae){let te=j+ae*v.strides[2],ie=ae*c.strideHeight-x;for(let Q=0;Q<h;++Q){let he=ie+Q*f;if(he<0||he>=c.inHeight)continue;let oe=ee+Q*E[1],fe=Y+he*N[2];for(let pe=0;pe<c.outWidth;++pe){let ke=te+pe*c.outChannels,Se=pe*c.strideWidth-g;for(let Fe=0;Fe<d;++Fe){let Oe=Se+Fe*m;if(Oe<0||Oe>=c.inWidth)continue;let $e=oe+Fe*E[2],et=fe+Oe*c.inChannels,tt=$e;for(let it=0;it<c.inChannels;++it){let Ke=w[et+it];for(let dt=0;dt<c.outChannels;++dt)k[ke+dt]+=Ke*b[tt+dt];tt+=c.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var nD={kernelName:Nu,backendName:"cpu",kernelFunc:tD};function rD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r;ve([a,s],"conv3dBackpropFilterV2");let c=_.computeStrides(a.shape),u=_.computeStrides(s.shape),h=C.computeConv3DInfo(a.shape,l,i,1,o),d=h.strideDepth,p=h.strideHeight,f=h.strideWidth,m=h.filterDepth,y=h.filterHeight,A=h.filterWidth,g=new Dt(h.filterShape,"float32"),x=g.values,[v,w,b,k]=g.strides,N=n.data.get(s.dataId).values,[E,F,O,L]=u,V=n.data.get(a.dataId).values,[j,U,X,G]=c,ee=h.padInfo.front,Y=h.padInfo.left,ae=h.padInfo.top;for(let te=0;te<m;++te){let ie=Math.max(0,Math.ceil((ee-te)/d)),Q=Math.min(h.outDepth,(h.inDepth+ee-te)/d),he=te*v;for(let oe=0;oe<y;++oe){let fe=Math.max(0,Math.ceil((ae-oe)/p)),pe=Math.min(h.outHeight,(h.inHeight+ae-oe)/p),ke=oe*w+he;for(let Se=0;Se<A;++Se){let Fe=Math.max(0,Math.ceil((Y-Se)/f)),Oe=Math.min(h.outWidth,(h.inWidth+Y-Se)/f),$e=Se*b+ke;for(let et=0;et<h.inChannels;++et){let tt=et*k+$e;for(let it=0;it<h.outChannels;++it){let Ke=0;for(let dt=0;dt<h.batchSize;++dt){let je=dt*j,_n=dt*E;for(let bt=ie;bt<Q;++bt){let Kn=(te+bt*d-ee)*U+je,Xt=bt*F+_n;for(let vn=fe;vn<pe;++vn){let Zn=(oe+vn*p-ae)*X+Kn,Dn=vn*O+Xt;for(let on=Fe;on<Oe;++on){let Kt=(Se+on*f-Y)*G+Zn,Or=on*L+Dn;Ke+=V[Kt+et]*N[Or+it]}}}}x[tt+it]=Ke}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var aD={kernelName:Wh,backendName:"cpu",kernelFunc:rD};function sD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;ve([a],"conv3dBackpropInputV2");let c=_.computeStrides(a.shape),u=_.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),d=new Dt(h.inShape,"float32"),p=d.values,[f,m,y,A]=d.strides,g=n.data.get(a.dataId).values,[x,v,w,b]=c,k=n.data.get(s.dataId).values,[N,E,F,O]=u,{batchSize:L,filterDepth:V,filterHeight:j,filterWidth:U,inChannels:X,inDepth:G,inHeight:ee,inWidth:Y,outChannels:ae,outDepth:te,outHeight:ie,outWidth:Q,strideDepth:he,strideHeight:oe,strideWidth:fe}=h,pe=V-1-h.padInfo.front,ke=j-1-h.padInfo.top,Se=U-1-h.padInfo.left;for(let Fe=0;Fe<L;++Fe)for(let Oe=0;Oe<X;++Oe)for(let $e=0;$e<G;++$e){let et=$e-pe,tt=Math.max(0,Math.ceil(et/he)),it=Math.min(te,(V+et)/he);for(let Ke=0;Ke<ee;++Ke){let dt=Ke-ke,je=Math.max(0,Math.ceil(dt/oe)),_n=Math.min(ie,(j+dt)/oe);for(let bt=0;bt<Y;++bt){let Kn=bt-Se,Xt=Math.max(0,Math.ceil(Kn/fe)),vn=Math.min(Q,(U+Kn)/fe),Zn=0;for(let Dn=tt;Dn<it;++Dn){let on=Dn*he-et;for(let Kt=je;Kt<_n;++Kt){let Or=Kt*oe-dt;for(let ir=Xt;ir<vn;++ir){let or=ir*fe-Kn,ba=x*Fe+v*Dn+w*Kt+b*ir,na=N*(V-1-on)+E*(j-1-Or)+F*(U-1-or)+O*Oe;for(let _a=0;_a<ae;++_a){let 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ee=Math.floor(G),Y=Math.ceil(G),ae=G-ee;for(let te=0;te<y;te++){let ie=y>1?F*(d-1)+te*U:.5*(F+L)*(d-1);if(ie<0||ie>d-1){for(let fe=0;fe<p;fe++){let pe=fe+te*b[2]+X*b[1]+k*b[0];A.values[pe]=c}continue}let Q=Math.floor(ie),he=Math.ceil(ie),oe=ie-Q;for(let fe=0;fe<p;fe++){let pe=fe+Q*w[2]+ee*w[1]+V*w[0],ke=v[pe];pe=fe+he*w[2]+ee*w[1]+V*w[0];let Se=v[pe];pe=fe+Q*w[2]+Y*w[1]+V*w[0];let Fe=v[pe];pe=fe+he*w[2]+Y*w[1]+V*w[0];let Oe=v[pe],$e=ke+(Se-ke)*oe,et=Fe+(Oe-Fe)*oe;pe=fe+te*b[2]+X*b[1]+k*b[0],A.values[pe]=$e+(et-$e)*ae}}}else for(let ee=0;ee<y;++ee){let Y=y>1?F*(d-1)+ee*U:.5*(F+L)*(d-1);if(Y<0||Y>d-1){for(let ie=0;ie<p;ie++){let Q=ie+ee*b[2]+X*b[1]+k*b[0];A.values[Q]=c}continue}let ae=Math.round(Y),te=Math.round(G);for(let ie=0;ie<p;ie++){let Q=ie+ae*w[2]+te*w[1]+V*w[0],he=ie+ee*b[2]+X*b[1]+k*b[0];A.values[he]=v[Q]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var dD={kernelName:go,backendName:"cpu",kernelFunc:hD};function pD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;ve(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=tr({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=C.getInnerMostAxes(1,a.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let h=dr(c.dtype,"int32"),d=_.makeZerosTypedArray(_.sizeFromShape(c.shape),h),p=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=o?(A,g)=>A+f-g-1:(A,g)=>A+g;for(let A=0;A<p.length;A+=f)for(let g=0;g<f;g++){let x=m(A,g);if(g===0)d[x]=i?0:p[x];else{let v=m(A,g-1);d[x]=i?p[v]+d[v]:p[x]+d[v]}}let y=n.makeTensorInfo(c.shape,h,d);if(l!=null){let A=C.getUndoAxesPermutation(l),g=tr({inputs:{x:y},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(c),g}return y}var fD={kernelName:bs,backendName:"cpu",kernelFunc:pD};function mD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=ty(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=Zb(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var yD={kernelName:Vh,backendName:"cpu",kernelFunc:mD};function AD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`),_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=a.shape[1],c=a.shape[2],u=a.shape[3],h=l*s,d=c*s,p=u/(s*s),f=n.data.get(a.dataId).values,m=new Float32Array(o*h*d*p),y=0;for(let A=0;A<o;++A)for(let g=0;g<h;++g){let x=Math.floor(g/s),v=g%s;for(let w=0;w<d;++w){let b=Math.floor(w/s),k=w%s,N=(v*s+k)*p;for(let E=0;E<p;++E){let F=E+N+u*(b+c*(x+l*A));m[y++]=f[F]}}}return n.makeTensorInfo([o,h,d,p],a.dtype,m)}var gD={kernelName:xo,backendName:"cpu",kernelFunc:AD};function C_(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r;ve([a,s],"depthwiseConv2DNative");let u=_.computeStrides(a.shape),h=_.computeStrides(s.shape),d=l;d==null&&(d=[1,1]),_.assert(C.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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wD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r;ve([a,s],"depthwiseConv2dNativeBackpropFilter");let h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:p,filterHeight:f,filterWidth:m}=h,y=new Dt(h.filterShape,"float32"),A=h.padInfo.left,g=h.padInfo.top,x=h.outChannels/h.inChannels,v=n.data.get(a.dataId).values,w=new Dt(a.shape,a.dtype,v),b=n.data.get(s.dataId).values,k=new Dt(s.shape,s.dtype,b);for(let N=0;N<f;++N){let E=Math.max(0,Math.ceil((g-N)/d)),F=Math.min(h.outHeight,(h.inHeight+g-N)/d);for(let O=0;O<m;++O){let L=Math.max(0,Math.ceil((A-O)/p)),V=Math.min(h.outWidth,(h.inWidth+A-O)/p);for(let j=0;j<h.outChannels;++j){let U=Math.trunc(j/x),X=j%x,G=0;for(let ee=0;ee<h.batchSize;++ee)for(let Y=E;Y<F;++Y){let ae=N+Y*d-g;for(let te=L;te<V;++te){let ie=O+te*p-A;G+=w.get(ee,ae,ie,U)*k.get(ee,Y,te,j)}}y.set(G,N,O,U,X)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var bD={kernelName:jh,backendName:"cpu",kernelFunc:wD};function _D(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r;ve([a,s],"depthwiseConv2DNativeBackpropInput");let h=_.computeStrides(a.shape),d=_.computeStrides(s.shape),p=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),f=new Dt(p.inShape,"float32"),m=f.values,[y,A,g]=f.strides,x=n.data.get(a.dataId).values,[v,w,b]=h,k=n.data.get(s.dataId).values,[N,E,F]=d,{batchSize:O,filterHeight:L,filterWidth:V,inChannels:j,inHeight:U,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,ie=L-1-p.padInfo.top,Q=V-1-p.padInfo.left,he=G/j;for(let oe=0;oe<O;++oe)for(let fe=0;fe<j;++fe)for(let pe=0;pe<U;++pe){let ke=pe-ie,Se=Math.max(0,Math.ceil(ke/ae)),Fe=Math.min(ee,(L+ke)/ae);for(let Oe=0;Oe<X;++Oe){let $e=Oe-Q,et=Math.max(0,Math.ceil($e/te)),tt=Math.min(Y,(V+$e)/te),it=0;for(let Ke=Se;Ke<Fe;++Ke){let dt=Ke*ae-ke;for(let je=et;je<tt;++je){let 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ID={kernelName:Hh,backendName:"cpu",kernelFunc:kD},SD={kernelName:Tu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(r.dataId).values,u=r.shape.length,h=l.data.get(a.dataId).values,d=a.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:y,outHeight:A,outWidth:g,padInfo:x,strideHeight:v,strideWidth:w,filterHeight:b,filterWidth:k,dilationHeight:N,dilationWidth:E,outShape:F}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),O=_.sizeFromShape(F),L=F.length,V=_.getArrayFromDType(r.dtype,O);for(let j=0;j<p;++j)for(let U=0;U<A;++U){let X=U*v-x.top;for(let G=0;G<g;++G){let ee=G*w-x.left;for(let Y=0;Y<y;++Y){let ae=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<b;++ie){let Q=X+ie*N;if(Q>=0&&Q<f)for(let he=0;he<k;++he){let oe=ee+he*E;if(oe>=0&&oe<m){let fe=_.locToIndex([j,Q,oe,Y],u,_.computeStrides(r.shape)),pe=_.locToIndex([ie,he,Y],d,_.computeStrides(a.shape)),ke=c[fe]+h[pe];ke>ae&&(ae=ke)}}}let te=_.locToIndex([j,U,G,Y],L,_.computeStrides(F));V[te]=ae}}}return{dataId:l.write(_.toTypedArray(V,r.dtype),F,r.dtype),shape:F,dtype:r.dtype}}},ND={kernelName:qh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=_.toNestedArray(r.shape,c.data.get(r.dataId).values),h=_.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:y,outWidth:A,padInfo:g,strideHeight:x,strideWidth:v,filterHeight:w,filterWidth:b,dilationHeight:k,dilationWidth:N,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);_.assert(s.rank===E.length,()=>`Error in ${qh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let F=_.toNestedArray(E,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(a.shape,a.dtype);for(let L=0;L<d;++L)for(let V=0;V<y;++V){let j=V*x-g.top;for(let U=0;U<A;++U){let X=U*v-g.left;for(let G=0;G<m;++G){let 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F=_.toNestedArray(E,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(r.shape,r.dtype);for(let L=0;L<d;++L)for(let V=0;V<y;++V){let j=V*x-g.top;for(let U=0;U<A;++U){let X=U*v-g.left;for(let G=0;G<m;++G){let ee=Number.MIN_SAFE_INTEGER,Y=j<0?0:j,ae=X<0?0:X;for(let te=0;te<w;++te){let ie=j+te*k;if(ie>=0&&ie<p)for(let Q=0;Q<b;++Q){let he=X+Q*N;if(he>=0&&he<f){let oe=u[L][ie][he][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=ie,ae=he)}}}O[L][Y][ae][G]+=F[L][V][U][G]}}}return{dataId:c.write(_.toTypedArray(O,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function fc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"sum");let o;a.dtype==="bool"?o=Ha({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Gr({inputs:{x:a},backend:n});let l=o.shape.length,c=_.parseAxisParam(s,o.shape),u=C.getAxesPermutation(c,l),h=c,d=o;u!=null&&(d=tr({inputs:{x:o},backend:n,attrs:{perm:u}}),h=C.getInnerMostAxes(h.length,l)),C.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,f]=C.computeOutAndReduceShapes(d.shape,h),m=C.upcastType(d.dtype,"int32"),y=up(n,p,m),A=_.sizeFromShape(f),g=n.data.get(y.dataId).values,x=n.data.get(d.dataId).values;for(let v=0;v<g.length;++v){let w=v*A,b=0;for(let k=0;k<A;++k)b+=x[w+k];g[v]=b}if(i){let v=C.expandShapeToKeepDim(y.shape,c),w=y;y=mt({inputs:{x:y},backend:n,attrs:{shape:v}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),y}var ED={kernelName:Qs,backendName:"cpu",kernelFunc:fc};function CD(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=C.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,f=[];for(let m=0;m<h;++m){for(let y of u[m]){let{permutationIndices:A,expandDims:g}=C.getEinsumPermutation(p,l[y]),x;C.isIdentityPermutation(A)?x=s[y]:(x=tr({inputs:{x:s[y]},backend:n,attrs:{perm:A}}),f.push(x));let v=x.shape.slice();for(let w=0;w<g.length;++w)v.splice(g[w],0,1);_.arraysEqual(x.shape,v)||(x=mt({inputs:{x},backend:n,attrs:{shape:v}}),f.push(x)),d===null?d=x:(d=cp({inputs:{a:x,b:d},backend:n}),f.push(d))}m<h-1&&(c[m]>=0&&(d=fc({inputs:{x:d},backend:n,attrs:{axis:c[m]-(i.length-p),keepDims:!1}}),f.push(d)),p--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var RD={kernelName:Xh,backendName:"cpu",kernelFunc:CD};function MD(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;ve([r,a],"eluGrad");let s=new Float32Array(_.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(a.shape,"float32",s)}var FD={kernelName:Kh,backendName:"cpu",kernelFunc:MD},$D=Mt((e,t)=>e===t?1:0),R_=Ut(_o,$D,null,"bool"),DD={kernelName:_o,backendName:"cpu",kernelFunc:R_},OD=C.ERF_P,zD=C.ERF_A1,PD=C.ERF_A2,LD=C.ERF_A3,WD=C.ERF_A4,BD=C.ERF_A5,VD=at(bo,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+OD*n);return t*(1-((((BD*r+WD)*r+LD)*r+PD)*r+zD)*r*Math.exp(-n*n))}),jD={kernelName:bo,backendName:"cpu",kernelFunc:VD};function hp(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(_.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),mt({inputs:{x:a},backend:n,attrs:{shape:o}})}var UD={kernelName:vo,backendName:"cpu",kernelFunc:hp},HD=Mt((e,t)=>e/t),cy=Ut(vs,HD),hy={kernelName:vs,backendName:"cpu",kernelFunc:cy};function M_(e,t,n){let r=e.shape,a=r[0],s=r[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,c=[a,s],u=_.sizeFromShape(c),h=_.getTypedArrayFromDType("float32",u),d=_.getTypedArrayFromDType("float32",u);for(let y=0;y<a;y++){let A=Ii({inputs:{x:o},backend:n,attrs:{begin:[y,0],size:[1,s]}}),g=Ii({inputs:{x:l},backend:n,attrs:{begin:[y,0],size:[1,s]}}),x=Bn({inputs:{real:A,imag:g},backend:n}),{real:v,imag:w}=GD(x,t,n),b=C.mergeRealAndImagArrays(v,w);for(let k=0;k<s;k++){let N=C.getComplexWithIndex(b,k);h[y*s+k]=N.real,d[y*s+k]=N.imag}n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x)}let p=n.makeTensorInfo(c,"float32",h),f=n.makeTensorInfo(c,"float32",d),m=Bn({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function GD(e,t,n){let r=_.sizeFromShape(e.shape),a=n.data.get(e.dataId),s=n.data.get(a.complexTensorInfos.real.dataId).values,i=n.data.get(a.complexTensorInfos.imag.dataId).values;if(qD(r)){let o=dy(s,i,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",o.real),u=n.makeTensorInfo(l,"float32",o.imag),h=n.makeTensorInfo([],"float32",_.createScalarValue(r,"float32")),d=Gr({inputs:{x:h},backend:n}),p=hy.kernelFunc({inputs:{a:c,b:h},backend:n}),f=hy.kernelFunc({inputs:{a:u,b:d},backend:n}),m=n.data.get(p.dataId).values,y=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),{real:m,imag:y}}return o}else{let o=C.mergeRealAndImagArrays(s,i),l=XD(o,r,t);return C.splitRealAndImagArrays(l)}}function qD(e){return(e&e-1)==0}function dy(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=C.mergeRealAndImagArrays(e,t),i=n/2,o=C.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],h=a.makeTensorInfo(u,"float32",l),d=a.makeTensorInfo(u,"float32",c),p=Bn({inputs:{real:h,imag:d},backend:a}),f=C.complexWithOddIndex(s),m=f.real,y=f.imag,A=[m.length],g=a.makeTensorInfo(A,"float32",m),x=a.makeTensorInfo(A,"float32",y),v=Bn({inputs:{real:g,imag:x},backend:a}),w=dy(l,c,i,r,a),b=w.real,k=w.imag,N=[b.length],E=a.makeTensorInfo(N,"float32",b),F=a.makeTensorInfo(N,"float32",k),O=Bn({inputs:{real:E,imag:F},backend:a}),L=dy(m,y,i,r,a),V=L.real,j=L.imag,U=[V.length],X=a.makeTensorInfo(U,"float32",V),G=a.makeTensorInfo(U,"float32",j),ee=Bn({inputs:{real:X,imag:G},backend:a}),Y=C.exponents(n,r),ae=[Y.real.length],te=a.makeTensorInfo(ae,"float32",Y.real),ie=a.makeTensorInfo(ae,"float32",Y.imag),Q=Bn({inputs:{real:te,imag:ie},backend:a}),he=cp({inputs:{a:Q,b:ee},backend:a}),oe=pc({inputs:{a:O,b:he},backend:a}),fe=oy({inputs:{a:O,b:he},backend:a}),pe=ki({inputs:{input:oe},backend:a}),ke=ki({inputs:{input:fe},backend:a}),Se=Rl({inputs:{input:oe},backend:a}),Fe=Rl({inputs:{input:fe},backend:a}),Oe=Ml({inputs:[pe,ke],backend:a,attrs:{axis:0}}),$e=Ml({inputs:[Se,Fe],backend:a,attrs:{axis:0}}),et=a.data.get(Oe.dataId).values,tt=a.data.get($e.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(v),a.disposeIntermediateTensorInfo(E),a.disposeIntermediateTensorInfo(F),a.disposeIntermediateTensorInfo(O),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(G),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(te),a.disposeIntermediateTensorInfo(ie),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(he),a.disposeIntermediateTensorInfo(oe),a.disposeIntermediateTensorInfo(fe),a.disposeIntermediateTensorInfo(pe),a.disposeIntermediateTensorInfo(Se),a.disposeIntermediateTensorInfo(ke),a.disposeIntermediateTensorInfo(Fe),a.disposeIntermediateTensorInfo(Oe),a.disposeIntermediateTensorInfo($e),{real:et,imag:tt}}function XD(e,t,n){let r=new Float32Array(t*2);for(let a=0;a<t;a++){let s=0,i=0;for(let o=0;o<t;o++){let 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Ce=J();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>aA(2)?2:aA(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>V_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>j_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ce.getNumber("WEBGL_VERSION");return e===0?0:U_(e)});Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Vu.isMobile());Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>H_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ce.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>G_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_FENCE_API_ENABLED",()=>q_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ce.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ce.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Vu.isMobile()&&Ce.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function dn(){let e,t,n,r,a,s,i,o,l,c;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
+`)[0]),console.log(`%c ${_.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
+`))}function G_(e){return ma(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function q_(e,t){if(we(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function pp(e,t){if(we(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function X_(e,t){let n=ma(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),we(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function K_(e,t){let n=ma(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),we(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function qP(){return J().getNumber("WEBGL_VERSION")===2?1:4}function Z_(e){return ma(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Y_(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${e}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){let r=`[${e}x${t}]`,a=`[${n}x${n}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+a+".")}}function J_(e){return ma(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function fy(e,t,n,r,a,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),we(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),we(e,()=>e.enableVertexAttribArray(o)),!0)}function Q_(e,t,n){h3(e,n),we(e,()=>e.activeTexture(e.TEXTURE0+n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function XP(e,t){h3(e,t),we(e,()=>e.activeTexture(e.TEXTURE0+t)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function e3(e,t,n){return ma(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function t3(e,t,n){return e.getUniformLocation(t,n)}function n3(e,t,n,r){we(e,()=>Q_(e,t,r)),we(e,()=>e.uniform1i(n,r))}function KP(e){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),we(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function fp(e,t,n){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function my(e,t){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Ac(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+r3(e,t))}function r3(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function ma(e,t,n){let r=we(e,()=>t());if(r==null)throw new Error(n);return r}function h3(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function Si(e,t=2){return _.sizeFromShape(e.slice(0,e.length-t))}function Ni(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function mp(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[Si(e),...Ni(e)]),t}function a3(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?_.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=_.squeezeShape(e).newShape);let r=_.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let a=Si(e),s=2,i=2;return e.length&&([s,i]=Ni(e)),r=a*(s/2)*(i/2),_.sizeToSquarishShape(r).map(o=>o*2)}return _.sizeToSquarishShape(r)}function Ap(e){return e%2==0}function gc(e,t){if(e=e.slice(-2),t=t.slice(-2),_.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r||Ap(n)&&Ap(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Ap(e[0])&&Ap(t[0])}var gp,xp;function s3(e){if(gp==null){let t=qr(e);gp=t.getParameter(t.MAX_TEXTURE_SIZE)}return gp}function ZP(){gp=null}function YP(){xp=null}function i3(e){if(xp==null){let t=qr(e);xp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,xp)}function o3(e){if(e===0)return 0;let t,n=qr(e);return nr(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:nr(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function nr(e,t){return e.getExtension(t)!=null}function yy(e){try{if(qr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function l3(e){if(e===0)return!1;let t=qr(e);if(e===1){if(!nr(t,"OES_texture_float"))return!1}else if(!nr(t,"EXT_color_buffer_float"))return!1;return xy(t)}function u3(e){if(e===0)return!1;let t=qr(e);if(e===1){if(!nr(t,"OES_texture_float")||!nr(t,"WEBGL_color_buffer_float"))return!1}else{if(nr(t,"EXT_color_buffer_float"))return xy(t);let n="EXT_color_buffer_half_float";if(nr(t,n)){let r=t.getExtension(n);return oL(t,r)}return!1}return xy(t)}function xy(e){let t=gy(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function oL(e,t){let n=gy(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function c3(e){return e!==2?!1:qr(e).fenceSync!=null}function Fl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&_.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ee=J();Ee.registerFlag("HAS_WEBGL",()=>Ee.getNumber("WEBGL_VERSION")>0);Ee.registerFlag("WEBGL_VERSION",()=>yy(2)?2:yy(1)?1:0);Ee.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ee.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ee.get("WEBGL_VERSION")===2);Ee.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ee.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ee.registerFlag("WEBGL_PACK",()=>Ee.getBool("HAS_WEBGL"));Ee.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_CLIP",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!0);Ee.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_REDUCE",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_CONV_IM2COL",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>s3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>i3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ee.getNumber("WEBGL_VERSION");return e===0?0:o3(e)});Ee.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ee.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!qu.isMobile());Ee.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>l3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ee.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ee.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ee.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>u3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_FENCE_API_ENABLED",()=>c3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ee.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ee.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ee.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>qu.isMobile()&&Ee.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function dn(){let e,t,n,r,a,s,i,o,l,c;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Ii(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function lA(e){let t=v.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Ei(e,t,n="index"){let r=_.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function wy(e){let t=_.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}var K_=`
+`}var d3=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -117,9 +117,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,LP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=fc.DENSE;let t=Ac(e),n=dn();this.outputShape=e,this.userCode=`
+`,lL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=xc.DENSE;let t=bc(e),n=dn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Ii(["r","c","d"],e)}
+        ${Ei(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -138,9 +138,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},WP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=fc.DENSE;let t=Ac(e),n=dn();this.outputShape=e,this.userCode=`
+    `}},uL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xc.DENSE;let t=bc(e),n=dn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Ii(["r","c","d"],e)}
+        ${Ei(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -159,23 +159,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},BP=class{constructor(e){this.variableNames=["A"],this.outTexUsage=tr.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
-      ${K_}
+    `}},cL=class{constructor(e){this.variableNames=["A"],this.outTexUsage=rr.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
+      ${d3}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},VP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=tr.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
-      ${K_}
+    `}},hL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=rr.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
+      ${d3}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},jP=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=dn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${lA(e)}
+    `}},dL=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=dn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${wy(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -204,7 +204,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}},UP=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=dn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
+    `}},pL=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=dn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
           localCoords = coords;
           if(localCoords[2] + ${c} < ${e[2]}) {
             localCoords[2] += ${c};
@@ -233,7 +233,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
             }
           }
         `}this.userCode=`
-      ${lA(e)}
+      ${wy(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -248,7 +248,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = ${o};
       }
-    `}},Z_={};Me(Z_,{bindVertexProgramAttributeStreams:()=>s3,createBufferFromOutputTexture:()=>l3,createFloat16MatrixTexture:()=>t3,createFloat16PackedMatrixTexture:()=>a3,createFloat32MatrixTexture:()=>e3,createIndexBuffer:()=>Q_,createPackedMatrixTexture:()=>r3,createUnsignedBytesMatrixTexture:()=>n3,createVertexBuffer:()=>J_,createVertexShader:()=>Y_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>c3,downloadFloat32MatrixFromBuffer:()=>u3,downloadMatrixFromPackedOutputTexture:()=>d3,downloadPackedMatrixFromBuffer:()=>h3,getInternalFormatForFloat16MatrixTexture:()=>cA,getInternalFormatForFloat16PackedMatrixTexture:()=>pA,getInternalFormatForFloat32MatrixTexture:()=>uA,getInternalFormatForPackedMatrixTexture:()=>dA,getInternalFormatForUnsignedBytesMatrixTexture:()=>hA,uploadDenseMatrixToTexture:()=>i3,uploadPixelDataToTexture:()=>o3});function Y_(e){let t=dn(),n=`${t.version}
+    `}},p3={};Me(p3,{bindVertexProgramAttributeStreams:()=>_3,createBufferFromOutputTexture:()=>I3,createFloat16MatrixTexture:()=>g3,createFloat16PackedMatrixTexture:()=>b3,createFloat32MatrixTexture:()=>A3,createIndexBuffer:()=>y3,createPackedMatrixTexture:()=>w3,createUnsignedBytesMatrixTexture:()=>x3,createVertexBuffer:()=>m3,createVertexShader:()=>f3,downloadByteEncodedFloatMatrixFromOutputTexture:()=>N3,downloadFloat32MatrixFromBuffer:()=>S3,downloadMatrixFromPackedOutputTexture:()=>E3,downloadPackedMatrixFromBuffer:()=>T3,getInternalFormatForFloat16MatrixTexture:()=>_y,getInternalFormatForFloat16PackedMatrixTexture:()=>Iy,getInternalFormatForFloat32MatrixTexture:()=>by,getInternalFormatForPackedMatrixTexture:()=>ky,getInternalFormatForUnsignedBytesMatrixTexture:()=>vy,uploadDenseMatrixToTexture:()=>v3,uploadPixelDataToTexture:()=>k3});function f3(e){let t=dn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -257,22 +257,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return S_(e,n)}function J_(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return R_(e,t)}function Q_(e){let t=new Uint16Array([0,1,2,2,1,3]);return M_(e,t)}function yc(e,t,n,r,a,s){D_(t,n);let i=F_(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function uA(e){return e.internalFormatFloat}function e3(e,t,n,r){let[a,s]=mc(t,n);return yc(e,a,s,uA(r),r.textureFormatFloat,e.FLOAT)}function cA(e){return e.internalFormatHalfFloat}function t3(e,t,n,r){let[a,s]=mc(t,n);return yc(e,a,s,cA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function hA(e){return e.downloadTextureFormat}function n3(e,t,n,r){let[a,s]=mc(t,n);return yc(e,a,s,hA(r),e.RGBA,e.UNSIGNED_BYTE)}function dA(e){return e.internalFormatPackedFloat}function r3(e,t,n,r){let[a,s]=El(t,n);return yc(e,a,s,dA(r),e.RGBA,e.FLOAT)}function pA(e){return e.internalFormatPackedHalfFloat}function a3(e,t,n,r){let[a,s]=El(t,n);return yc(e,a,s,pA(r),e.RGBA,r.textureTypeHalfFloat)}function s3(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),nA(e,t,"clipSpacePos",n,3,s,r)&&nA(e,t,"uv",n,2,s,a)}function i3(e,t,n,r,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function o3(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function l3(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function u3(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function c3(e,t,n,r){let[a,s]=mc(t,n),i=4,o=new Uint8Array(RP(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function h3(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(MP(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function d3(e,t,n){let r=new Float32Array(t*n*4);return we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var Ap=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,dp(t,e)):this.gl=qr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=hc(this.gl,a),er(this.gl,s))this.textureHalfFloatExtension=hc(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),er(this.gl,r))this.colorBufferHalfFloatExtension=hc(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",er(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(er(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=J_(this.gl),this.indexBuffer=Q_(this.gl),this.framebuffer=$_(this.gl),this.textureConfig=iA(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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-`)}function Cl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return tL(e);case 1:return nL(e);case 2:return rL(e);case 3:return aL(e);case 4:return sL(e);case 5:return iL(e);case 6:return oL(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function f3(e){switch(e.shapeInfo.logicalShape.length){case 0:return lL(e);case 1:return uL(e);case 2:return cL(e);case 3:return hL(e);default:return dL(e)}}function GP(e,t,n=!1){let r="";n?r+=f3(e):r+=Cl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=pL(e,t):r+=fL(e,t)),r}function qP(e,t){switch(e.length){case 0:return m3();case 1:return mL(e,t);case 2:return gL(e,t);case 3:return AL(e,t);default:return yL(e,t)}}function XP(e,t){switch(e.length){case 0:return m3();case 1:return xL(e,t);case 2:return kL(e,t);case 3:return wL(e,t);case 4:return bL(e,t);case 5:return _L(e,t);case 6:return vL(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function KP(e){return`
+    }`;return U_(e,n)}function m3(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return X_(e,t)}function y3(e){let t=new Uint16Array([0,1,2,2,1,3]);return K_(e,t)}function _c(e,t,n,r,a,s){Y_(t,n);let i=Z_(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function by(e){return e.internalFormatFloat}function A3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,by(r),r.textureFormatFloat,e.FLOAT)}function _y(e){return e.internalFormatHalfFloat}function g3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,_y(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function vy(e){return e.downloadTextureFormat}function x3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,vy(r),e.RGBA,e.UNSIGNED_BYTE)}function ky(e){return e.internalFormatPackedFloat}function w3(e,t,n,r){let[a,s]=$l(t,n);return _c(e,a,s,ky(r),e.RGBA,e.FLOAT)}function Iy(e){return e.internalFormatPackedHalfFloat}function b3(e,t,n,r){let[a,s]=$l(t,n);return _c(e,a,s,Iy(r),e.RGBA,r.textureTypeHalfFloat)}function _3(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),fy(e,t,"clipSpacePos",n,3,s,r)&&fy(e,t,"uv",n,2,s,a)}function v3(e,t,n,r,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function k3(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function I3(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function S3(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function N3(e,t,n,r){let[a,s]=wc(t,n),i=4,o=new Uint8Array(eL(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function T3(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(tL(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function E3(e,t,n){let r=new Float32Array(t*n*4);return we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var wp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,yp(t,e)):this.gl=qr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=yc(this.gl,a),nr(this.gl,s))this.textureHalfFloatExtension=yc(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),nr(this.gl,r))this.colorBufferHalfFloatExtension=yc(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",nr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(nr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=m3(this.gl),this.indexBuffer=y3(this.gl),this.framebuffer=J_(this.gl),this.textureConfig=gy(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;we(e,()=>e.finish()),we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.deleteFramebuffer(this.framebuffer)),we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),we(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),A3(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),g3(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),x3(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),k3(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),v3(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),b3(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),w3(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(my(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>N3(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return T3(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return S3(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=I3(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>E3(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=H_(t,e),r=f3(t),a=G_(t);return we(t,()=>t.attachShader(a,r)),we(t,()=>t.attachShader(a,n)),q_(t,a),this.debug&&pp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=_3(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&pp(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?e3(this.gl,e,t):t3(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),n3(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=$l(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&pp(this.gl,this.program),Ac(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),we(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),we(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=yc(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await _.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=fL(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&_.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),fp(this.gl,e,this.framebuffer),this.debug&&Ac(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(fp(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ac(this.gl)):my(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;fp(r,e,this.framebuffer),this.debug&&Ac(r),this.outputTexture=e,we(r,()=>r.viewport(0,0,t,n)),we(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function fL(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:C3}=C;function vL(e,t,n,r){let a=[];e.forEach(p=>{let f=_.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
+`),i=e.map(p=>mL(p,t,r)).join(`
+`),o=t.texShape,l=dn(),c=gL(l),u,h,d=bL(l);return t.isPacked?(u=yL(t.logicalShape,o),h=wL(l)):(u=AL(t.logicalShape,o),h=xL(l)),r&&(d+=_L),[d,c,h,s,u,i,n].join(`
+`)}function Dl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return kL(e);case 1:return IL(e);case 2:return SL(e);case 3:return NL(e);case 4:return TL(e);case 5:return EL(e);case 6:return CL(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function R3(e){switch(e.shapeInfo.logicalShape.length){case 0:return RL(e);case 1:return ML(e);case 2:return FL(e);case 3:return $L(e);default:return DL(e)}}function mL(e,t,n=!1){let r="";n?r+=R3(e):r+=Dl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=OL(e,t):r+=zL(e,t)),r}function yL(e,t){switch(e.length){case 0:return M3();case 1:return PL(e,t);case 2:return BL(e,t);case 3:return LL(e,t);default:return WL(e,t)}}function AL(e,t){switch(e.length){case 0:return M3();case 1:return VL(e,t);case 2:return qL(e,t);case 3:return jL(e,t);case 4:return UL(e,t);case 5:return HL(e,t);case 6:return GL(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function gL(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function ZP(e){return`
+  `}function xL(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function YP(e){return`
+  `}function wL(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function JP(e){return`${e.version}
+  `}function bL(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${IL}
-    ${NL}
-    ${SL}
-  `}var IL=`
+    ${XL}
+    ${KL}
+    ${ZL}
+  `}var XL=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,NL=`
+`,KL=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,SL=`
+`,ZL=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,QP=`
+`,_L=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -370,11 +370,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function m3(){return`
+`;function M3(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function mL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
+  `}function PL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ${n[1]}.0);
       }
@@ -388,7 +388,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function xL(e,t){return t[0]===1?`
+  `}function VL(e,t){return t[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${t[1]}.0);
       }
@@ -402,7 +402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function AL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
+  `}function LL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${n[0]}, ${n[1]}));
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function wL(e,t){let n=Ii(["r","c","d"],e);return`
+  `}function jL(e,t){let n=Ei(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function yL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
+  `}function WL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+i,o=`b${l}, `+o;return`
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${o});
     }
-  `}function bL(e,t){let n=Ii(["r","c","d","d2"],e);return`
+  `}function UL(e,t){let n=Ei(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -451,7 +451,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function _L(e,t){let n=Ii(["r","c","d","d2","d3"],e);return`
+  `}function HL(e,t){let n=Ei(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function vL(e,t){let n=Ii(["r","c","d","d2","d3","d4"],e);return`
+  `}function GL(e,t){let n=Ei(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -474,7 +474,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function gL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
+  `}function BL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(_.arraysEqual(e,t))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
       }
@@ -489,7 +489,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function kL(e,t){return v.arraysEqual(e,t)?`
+  `}function qL(e,t){return _.arraysEqual(e,t)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
@@ -516,34 +516,34 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Ni(e){return`offset${e}`}function lL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=dn();return`
+  `}function Ci(e){return`offset${e}`}function RL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=dn();return`
     vec4 ${n}() {
       return ${r.texture2D}(${t}, halfCR);
     }
-  `}function tL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+  `}function kL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
-    `;let[s,i]=e.shapeInfo.texShape,o=Ni(t);return`
+    `;let[s,i]=e.shapeInfo.texShape,o=Ci(t);return`
     float ${n}() {
       vec2 uv = uvFromFlat(${s}, ${i}, ${o});
       return sampleTexture(${t}, uv);
     }
-  `}function uL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=dn();return`
+  `}function ML(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=dn();return`
     vec4 ${n}(int index) {
       vec2 uv = packedUVfrom1D(
         ${a[0]}, ${a[1]}, index);
       return ${s.texture2D}(${t}, uv);
     }
-  `}function nL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function IL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let r=e.shapeInfo.texShape,a=r[0],s=r[1];if(s===1&&a===1)return`
       float ${n}(int index) {
         return sampleTexture(${t}, halfCR);
       }
-    `;let i=Ni(t);return s===1?`
+    `;let i=Ci(t);return s===1?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
         return sampleTexture(${t}, uv);
@@ -558,7 +558,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
       return sampleTexture(${t}, uv);
     }
-  `}function cL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=dn();if(a!=null&&v.arraysEqual(t,a))return`
+  `}function FL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=dn();if(a!=null&&_.arraysEqual(t,a))return`
       vec4 ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
 
@@ -569,22 +569,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function rL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
+  `}function SL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&_.arraysEqual(t,a)){let h=a[0],d=a[1];return`
     float ${r}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
       return sampleTexture(${n}, uv);
     }
-  `}let{newShape:s,keptDims:i}=v.squeezeShape(t),o=s;if(o.length<t.length){let h=Ml(e,o),d=["row","col"];return`
-      ${Cl(h)}
+  `}let{newShape:s,keptDims:i}=_.squeezeShape(t),o=s;if(o.length<t.length){let h=zl(e,o),d=["row","col"];return`
+      ${Dl(h)}
       float ${r}(int row, int col) {
-        return ${r}(${Fl(d,i)});
+        return ${r}(${Pl(d,i)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
-        ${Rl(e)}
+        ${Ol(e)}
       }
-    `;let l=a[0],c=a[1],u=Ni(n);return c===1?`
+    `;let l=a[0],c=a[1],u=Ci(n);return c===1?`
     float ${r}(int row, int col) {
       float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
@@ -603,10 +603,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${n}, uv);
   }
-`}function hL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Ml(e,h),f=["b","row","col"];return`
-        ${f3(p)}
+`}function $L(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=zl(e,h),f=["b","row","col"];return`
+        ${R3(p)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${Fl(f,d)});
+          return ${r}(${Pl(f,d)});
         }
       `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=dn();return`
     vec4 ${r}(int b, int row, int col) {
@@ -614,16 +614,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${i}, ${o}, ${c}, ${l}, b, row, col);
       return ${u.texture2D}(${n}, uv);
     }
-  `}function aL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Ml(e,l),m=["row","col","depth"];return`
-        ${Cl(f)}
+  `}function NL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=_.squeezeShape(t),l=i;if(l.length<t.length){let f=zl(e,l),m=["row","col","depth"];return`
+        ${Dl(f)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${Fl(m,o)});
+          return ${r}(${Pl(m,o)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${s}, 1)));
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
         float ${r}(int row, int col, int depth) {
@@ -640,14 +640,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
       return sampleTexture(${n}, uv);
     }
-  `;let p=Ni(n);return`
+  `;let p=Ci(n);return`
       float ${r}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${a} + col * ${s} + depth + ${p};
         vec2 uv = uvFromFlat(${u}, ${h}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function dL(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=dn();return`
+  `}function DL(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=dn();return`
     vec4 ${a}(${h}) {
       int index = ${d};
       int texR = index / ${l};
@@ -655,16 +655,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
       return ${p.texture2D}(${r}, uv);
     }
-  `}function sL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=Ml(e,o),m=["row","col","depth","depth2"];return`
-      ${Cl(f)}
+  `}function TL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=_.squeezeShape(t);if(o.length<t.length){let f=zl(e,o),m=["row","col","depth","depth2"];return`
+      ${Dl(f)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${Fl(m,l)});
+        return ${r}(${Pl(m,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${i}, ${s}, ${a}, 1)));
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
       float ${r}(int row, int col, int depth, int depth2) {
@@ -685,7 +685,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${d}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let p=Ni(n);return`
+    `;let p=Ci(n);return`
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${i} + col * ${s} +
@@ -693,10 +693,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
       return sampleTexture(${n}, uv);
     }
-  `}function iL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=Ml(e,l),A=["row","col","depth","depth2","depth3"];return`
-      ${Cl(m)}
+  `}function EL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=_.squeezeShape(t);if(l.length<t.length){let m=zl(e,l),y=["row","col","depth","depth2","depth3"];return`
+      ${Dl(m)}
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        return ${r}(${Fl(A,c)});
+        return ${r}(${Pl(y,c)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
@@ -704,7 +704,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${s}, ${a})) +
           depth3;
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
@@ -726,7 +726,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${p}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let f=Ni(n);return`
+    `;let f=Ci(n);return`
     float ${r}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${o} + col * ${i} + depth * ${s} +
@@ -734,11 +734,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${p}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function oL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Ml(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
-      ${Cl(A)}
+  `}function CL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=_.squeezeShape(t);if(a.length<t.length){let y=zl(e,a),A=["row","col","depth","depth2","depth3","depth4"];return`
+      ${Dl(y)}
       float ${r}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${r}(${Fl(y,s)});
+        return ${r}(${Pl(A,s)});
       }
     `}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth,
@@ -749,7 +749,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===u&&h==null)return`
       float ${r}(int row, int col, int depth,
@@ -775,7 +775,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${p}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let m=Ni(n);return`
+    `;let m=Ci(n);return`
     float ${r}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -784,42 +784,42 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${f}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function Rl(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function Ol(e){let t=e.name,n=_.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
       }
     }
-  `}function pL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=p3(e.shapeInfo.logicalShape,t.logicalShape),l=ot(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
-`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
+  `}function OL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=C3(e.shapeInfo.logicalShape,t.logicalShape),l=lt(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(y=>`coords.${h[y+c]} = 0;`).join(`
+`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((y,A)=>`coords.${h[A+c]}`).join(", ");let p="return outputValue;",f=_.sizeFromShape(e.shapeInfo.logicalShape)===1,m=_.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(f&&!m)i===1?p=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
       `:p=`
         return vec4(outputValue.x);
-      `;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
+      `;else if(o.length){let y=s-2,A=s-1;o.indexOf(y)>-1&&o.indexOf(A)>-1?p="return vec4(outputValue.x);":o.indexOf(y)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(A)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
     vec4 ${a}() {
       ${l} coords = getOutputCoords();
       ${u}
       vec4 outputValue = get${r}(${d});
       ${p}
     }
-  `}function fL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
+  `}function zL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&_.arraysEqual(i,s))return`
       float ${a}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let c=ot(l),u=p3(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(m=>`coords.${p[m+h]} = 0;`).join(`
-`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
+    `;let c=lt(l),u=C3(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(m=>`coords.${p[m+h]} = 0;`).join(`
+`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,y)=>`coords.${p[y+h]}`).join(", "),`
     float ${a}() {
       ${c} coords = getOutputCoords();
       ${d}
       return get${r}(${f});
     }
-  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ml(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Fl(e,t){return t.map(n=>e[n]).join(", ")}function TL(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=eL(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function A3(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function EL(e,t,n,r,a){A3(t.inShapeInfos,n),A3([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function CL(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:RL,bincountImpl:y3,bincountReduceImpl:ML,ceilImpl:FL,concatImpl:DL,expImpl:$L,expm1Impl:OL,floorImpl:zL,gatherV2Impl:PL,greaterImpl:LL,lessImpl:WL,linSpaceImpl:BL,logImpl:VL,maxImpl:jL,maximumImpl:UL,minimumImpl:HL,multiplyImpl:GL,negImpl:qL,prodImpl:XL,rangeImpl:KL,rsqrtImpl:ZL,simpleAbsImpl:g3,sliceImpl:YL,stridedSliceImpl:JL,subImpl:QL,tileImpl:eW,topKImpl:tW,transposeImpl:fA,uniqueImpl:nW}=Bm;function x3(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function pn(e,t){return t===1?[e]:x3(e,t)}function rW(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var oW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
+  `}function lt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function zl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Pl(e,t){return t.map(n=>e[n]).join(", ")}function YL(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=vL(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function F3(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!_.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!_.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function JL(e,t,n,r,a){F3(t.inShapeInfos,n),F3([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(_.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function QL(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:eW,bincountImpl:$3,bincountReduceImpl:tW,ceilImpl:nW,concatImpl:rW,expImpl:aW,expm1Impl:sW,floorImpl:iW,gatherV2Impl:oW,greaterImpl:lW,lessImpl:uW,linSpaceImpl:cW,logImpl:hW,maxImpl:dW,maximumImpl:pW,minimumImpl:fW,multiplyImpl:mW,negImpl:yW,prodImpl:AW,rangeImpl:gW,rsqrtImpl:xW,simpleAbsImpl:D3,sliceImpl:wW,stridedSliceImpl:bW,subImpl:_W,tileImpl:vW,topKImpl:kW,transposeImpl:Sy,uniqueImpl:IW}=ey;function O3(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function pn(e,t){return t===1?[e]:O3(e,t)}function SW(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var CW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let n=pn("rc",t),r=ot(t),a=aW(t,e,n),s=sW(t,e[e.length-1],e[e.length-2],n),i=iW(e,n);this.userCode=`
+      `;else{let n=pn("rc",t),r=lt(t),a=NW(t,e,n),s=TW(t,e[e.length-1],e[e.length-2],n),i=EW(e,n);this.userCode=`
         void main() {
           ${r} rc = getOutputCoords();
 
@@ -831,7 +831,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function lW(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function aW(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function sW(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
+      `}}};function RW(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function NW(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function TW(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
     int r = ${a[0]};
     int c = ${a[1]};
     int rp1 = r + 1;
@@ -839,12 +839,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
     bool cEdge = cp1 >= ${t};
     bool rEdge = rp1 >= ${n};
-  `}function iW(e,t){let n=e.length,r=lW(n,t);return n===1?`getA(rc),
+  `}function EW(e,t){let n=e.length,r=RW(n,t);return n===1?`getA(rc),
             rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
             0, 0`:`getA(${r[0]}),
           cEdge ? 0. : getA(${r[1]}),
           rEdge ? 0. : getA(${r[2]}),
-          rEdge || cEdge ? 0. : getA(${r[3]})`}var w3=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
+          rEdge || cEdge ? 0. : getA(${r[3]})`}var z3=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
         ${a}
         ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -856,8 +856,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${r>0?"}":""}
       `}this.userCode=`
-      ${uW(t)}
-      ${lA(e)}
+      ${MW(t)}
+      ${wy(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -872,12 +872,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function uW(e){return`
+    `}};function MW(e){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${Ii(["r","c","d"],e)}
+      ${Ei(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var cW=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=_3(t,n),a=v3(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=b3(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===tn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===tn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===tn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===tn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===tn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=_3(n,r),s=v3(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=b3(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function hW(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function b3(e,t,n,r,a){let s=dW(t,r),i;if(a){let[l,c]=El(e[0],e[1]);i=l*c}else{let[l,c]=mc(e[0],e[1]);i=l*c}let o=hW(n,s);return i*o}function dW(e,t){switch(e){case tn.PACKED_2X2_FLOAT32:return dA(t);case tn.PACKED_2X2_FLOAT16:return pA(t);case tn.UNPACKED_FLOAT32:return uA(t);case tn.UNPACKED_FLOAT16:return cA(t);case tn.PACKED_4X1_UNSIGNED_BYTE:return hA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function pW(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?tn.PACKED_2X2_FLOAT32:tn.UNPACKED_FLOAT32:e?tn.PACKED_2X2_FLOAT16:tn.UNPACKED_FLOAT16}function _3(e,t){if(e===tr.UPLOAD)return tn.PACKED_2X2_FLOAT32;if(e===tr.RENDER||e==null)return pW(t);if(e===tr.DOWNLOAD||e===tr.PIXELS)return tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function v3(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ha=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var FW=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=L3(t,n),a=W3(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=P3(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===en.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===en.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===en.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===en.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===en.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=L3(n,r),s=W3(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=P3(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function $W(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function P3(e,t,n,r,a){let s=DW(t,r),i;if(a){let[l,c]=$l(e[0],e[1]);i=l*c}else{let[l,c]=wc(e[0],e[1]);i=l*c}let o=$W(n,s);return i*o}function DW(e,t){switch(e){case en.PACKED_2X2_FLOAT32:return ky(t);case en.PACKED_2X2_FLOAT16:return Iy(t);case en.UNPACKED_FLOAT32:return by(t);case en.UNPACKED_FLOAT16:return _y(t);case en.PACKED_4X1_UNSIGNED_BYTE:return vy(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function OW(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?en.PACKED_2X2_FLOAT32:en.UNPACKED_FLOAT32:e?en.PACKED_2X2_FLOAT16:en.UNPACKED_FLOAT16}function L3(e,t){if(e===rr.UPLOAD)return en.PACKED_2X2_FLOAT32;if(e===rr.RENDER||e==null)return OW(t);if(e===rr.DOWNLOAD||e===rr.PIXELS)return en.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function W3(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ga=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Nr="if (isnan(x)) return x;",fW="return x;",k3="return abs(x);",mW="return (x >= 0.0) ? x : (exp(x) - 1.0);",AW=Nr+`
+    `}},Sr="if (isnan(x)) return x;",zW="return x;",B3="return abs(x);",PW="return (x >= 0.0) ? x : (exp(x) - 1.0);",LW=Sr+`
   return (x < 0.0) ? 0.0 : x;
-`,yW=Nr+`
+`,WW=Sr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,yp="return x;",gW="return x;",xW=`
+`,bp="return x;",BW="return x;",VW=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,wW=`
+`,jW=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,bW=`
+`,UW=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -921,7 +921,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Dl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+`,Ll=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -932,17 +932,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},_W=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=pn("rc",t),r=ot(t),a=rW(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},HW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=pn("rc",t),r=lt(t),a=SW(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${r} rc = getOutputCoords();
         vec4 packedInput = getA(${a});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},vW=Hr.whereImpl,kW=1e-7,IW=1e-4,mA={};function NW(e){return e in mA||(mA[e]={}),mA[e]}var SW=128,TW=600;function EW(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*TW/1024/1024}var $l=class extends fu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=qr(J().getNumber("WEBGL_VERSION"));this.binaryCache=NW(J().getNumber("WEBGL_VERSION")),this.gpgpu=new Ap(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new cW(this.gpgpu),this.numMBBeforeWarning=EW(),this.texData=new Nh(this,Lr())}nextDataId(){return $l.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:tr.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:tr.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Dl(i,yp):h=new Ha(i,yp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=R.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Dl(r,yp):p=new Ha(r,yp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...Ac(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=R.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Lr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!I_(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...Ac(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?hp(t):t,o=s?new VP(i):new BP(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Lr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=SW){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return vW(e.shape,t)}packedUnaryOp(e,t,n){let r=new Dl(e.shape,t),a=this.compileAndRun(r,[e],n);return Lr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=g3(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,k3,e.dtype);let t=new Ha(e.shape,k3),n=this.compileAndRun(t,[e]);return Lr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Lr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new _W(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new oW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[_i(e.shape),...vi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[_i(t),...vi(t)],s=new w3(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=hp(r),i;n?i=new WP(s):i=new LP(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===fc.DENSE){let m=Ac(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!pc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=CL(e,l,c),h=this.getAndSaveBinary(u,()=>TL(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),EL(this.gpgpu,h,l,c,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(ge(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?kW:IW}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=B_(n,o),t.texShape=u),a!=null){let h=hp(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=El(u[0],u[1]),d=new UP(h,[f,p],m)):d=new jP(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=tr.PIXELS:this.texData.get(A.dataId).usage=tr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=CW(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};$l.nextDataId=0;function CW(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var I3="3.3.0";function N3(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Vu.isBrowser()&&cl("webgl",()=>new $l,2);var RW={forceHalfFloat:N3},S3=`
+    `}},GW=Hr.whereImpl,qW=1e-7,XW=1e-4,Ny={};function KW(e){return e in Ny||(Ny[e]={}),Ny[e]}var ZW=128,YW=600;function JW(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*YW/1024/1024}var Wl=class extends wu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=qr(J().getNumber("WEBGL_VERSION"));this.binaryCache=KW(J().getNumber("WEBGL_VERSION")),this.gpgpu=new wp(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new FW(this.gpgpu),this.numMBBeforeWarning=JW(),this.texData=new Rh(this,ua())}nextDataId(){return Wl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:rr.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:rr.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Ll(i,bp):h=new Ga(i,bp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=_.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=C.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=_.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Ll(r,bp):p=new Ga(r,bp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...bc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=C.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=_.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ua().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>_.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!V_(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=_.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...bc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?mp(t):t,o=s?new hL(i):new cL(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=_.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=_.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=_.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:_.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=_.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=ZW){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&_.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return GW(e.shape,t)}packedUnaryOp(e,t,n){let r=new Ll(e.shape,t),a=this.compileAndRun(r,[e],n);return ua().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=D3(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,B3,e.dtype);let t=new Ga(e.shape,B3),n=this.compileAndRun(t,[e]);return ua().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&_.isString(n[0])){let a=n.map(s=>_.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return ua().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new HW(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new CW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Si(e.shape),...Ni(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[Si(t),...Ni(t)],s=new z3(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=mp(r),i;n?i=new uL(s):i=new lL(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===xc.DENSE){let m=bc(e.outputShape);i.texShape=m.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),_.sizeFromShape(s.shape)===0)return i.values=_.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(m.dataId);if(y.texture==null){if(!e.packedInputs&&_.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=m.shape)}else if(!!y.isPacked!=!!e.packedInputs)m=y.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),y=this.texData.get(m.dataId);else if(y.isPacked&&!gc(y.shape,m.shape)){let A=m,g=m.shape;m.shape=y.shape,m=this.packedReshape(m,g),o.push(m),y=this.texData.get(m.dataId),A.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:y,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=QL(e,l,c),h=this.getAndSaveBinary(u,()=>YL(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),JL(this.gpgpu,h,l,c,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=_.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?qW:XW}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=_.now());let u=t.texShape;if(u==null&&(u=a3(n,o),t.texShape=u),a!=null){let h=mp(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=$l(u[0],u[1]),d=new pL(h,[f,p],m)):d=new dL(h,[f,p],m);let y=this.makeTensorInfo([f,p],r);m?this.texData.get(y.dataId).usage=rr.PIXELS:this.texData.get(y.dataId).usage=rr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),p,f,a);let A=!0,g=this.runWebGLProgram(d,[y],r,null,A),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=_.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=QW(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*_.bytesPerElement(t)}};Wl.nextDataId=0;function QW(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var V3="3.4.0";function j3(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}qu.isBrowser()&&fl("webgl",()=>new Wl,2);var eB={forceHalfFloat:j3},U3=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,Ol=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
+`,Bl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -952,17 +952,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},gp=`
+    `}},_p=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`,gc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=`
+`,vc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||_.sizeFromShape(this.outputShape)===1)s=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
         `;else if(s=`
-          ${ot(a)} coords = getOutputCoords();
+          ${lt(a)} coords = getOutputCoords();
         `,a===1)s+=`
             result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
             result.z = 0.;
@@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Wn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var MW={kernelName:Ss,backendName:"webgl",kernelFunc:Wn};function Ga(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Wn({inputs:{x:r},backend:n}),l=Wn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var FW={kernelName:$h,backendName:"webgl",kernelFunc:Ga},T3="return (a < 0.) ? b * a : a;",E3=`
+    `}};function Vn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var tB={kernelName:Es,backendName:"webgl",kernelFunc:Vn};function qa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Vn({inputs:{x:r},backend:n}),l=Vn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var nB={kernelName:Ph,backendName:"webgl",kernelFunc:qa},H3="return (a < 0.) ? b * a : a;",G3=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function DW(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new gc(E3,a.shape,i.shape):new Ol(T3,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var $W={kernelName:Ts,backendName:"webgl",kernelFunc:DW},C3="return (a < 0.) ? b * a : a;",R3=`
+`;function rB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",_.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(G3,a.shape,i.shape):new Bl(H3,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var aB={kernelName:Cs,backendName:"webgl",kernelFunc:rB},q3="return (a < 0.) ? b * a : a;",X3=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function OW(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new gc(R3,r.shape,a.shape):new Ol(C3,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var zW={kernelName:Ws,backendName:"webgl",kernelFunc:OW},M3="if (isnan(x)) return x;",PW=`
+`;function sB(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(X3,r.shape,a.shape):new Bl(q3,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var iB={kernelName:js,backendName:"webgl",kernelFunc:sB},K3="if (isnan(x)) return x;",oB=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,LW=`
+`,lB=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Dl(i.shape,t):u=new Ha(i.shape,e),o.runWebGLProgram(u,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:c.shape},T=new Ol(e,l.shape,c.shape);return u.runWebGLProgram(T,[x,N],cr(b.dtype,_.dtype))}),g=Ga({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||cr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),w=u.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new gc(t,l.shape,c.shape,n):p=new Ol(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function xp(e,t=!1){if(e==="linear")return t?gW:fW;if(e==="relu")return t?wW:AW;if(e==="elu")return t?xW:mW;if(e==="relu6")return t?bW:yW;if(e==="prelu")return t?R3:C3;if(e==="leakyrelu")return t?E3:T3;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var F3=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Ll(i.shape,t):u=new Ga(i.shape,e),o.runWebGLProgram(u,[i],l)}}function tn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[y,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[v,w]=x,b={dataId:v.dataId,dtype:v.dtype,shape:l.shape},k={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new Bl(e,l.shape,c.shape);return u.runWebGLProgram(N,[b,k],dr(v.dtype,w.dtype))}),g=qa({inputs:{real:y,imag:A},backend:u});return u.disposeIntermediateTensorInfo(y),u.disposeIntermediateTensorInfo(A),g}let h=s||dr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[y,A]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(A,h),x=u.texData.get(g.dataId);return x.values=y,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new vc(t,l.shape,c.shape,n):p=new Bl(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function vp(e,t=!1){if(e==="linear")return t?BW:zW;if(e==="relu")return t?jW:LW;if(e==="elu")return t?VW:PW;if(e==="relu6")return t?UW:WW;if(e==="prelu")return t?X3:q3;if(e==="leakyrelu")return t?G3:H3;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Z3=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",y="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1011,7 +1011,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           ${i}
         }`:m=`vec4 activation(vec4 x) {
           ${i}
-        }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",w="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
+        }`,y="result = activation(result);");let A=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
       ${m}
 
       const float sharedDimension = ${u}.0;
@@ -1020,7 +1020,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         vec4 result = vec4(0);
         for (int i = 0; i < ${u}; i++) {
           int batchA = ${g};
-          int batchB = ${w};
+          int batchB = ${x};
           vec4 a = getMatrixA(batchA, ${h});
           vec4 b = getMatrixB(batchB, ${d});
 
@@ -1036,13 +1036,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ivec3 rc = getOutputCoords();
         vec4 result = dot2x2ARowBCol(rc);
 
-        ${y}
-
         ${A}
 
+        ${y}
+
         setOutput(result);
       }
-    `}},D3={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},$3=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},Y3={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},J3=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},O3="return a * b;";function z3(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new $3(D3.REAL,r.shape,a.shape),u=new $3(D3.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Ga({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=GL(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new gc(O3,r.shape,a.shape):i=new Ol(O3,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var WW={kernelName:Os,backendName:"webgl",kernelFunc:z3};function BW(e,t,n){let r=[_i(e.shape),...vi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[_i(t),...vi(t)],i=new w3(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),c=v.sizeFromShape(l);v.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!pc(a.shape,l)&&!(u.texture!==null&&pc(u.shape,l))?BW(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var VW={kernelName:Bo,backendName:"webgl",kernelFunc:xe},P3=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
+    `}},Q3="return a * b;";function Ty(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new J3(Y3.REAL,r.shape,a.shape),u=new J3(Y3.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=mW(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new vc(Q3,r.shape,a.shape):i=new Bl(Q3,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var uB={kernelName:Ls,backendName:"webgl",kernelFunc:Ty};function cB(e,t,n){let r=[Si(e.shape),...Ni(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[Si(t),...Ni(t)],i=new z3(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=_.sizeFromShape(a.shape),l=_.inferFromImplicitShape(s,o),c=_.sizeFromShape(l);_.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!gc(a.shape,l)&&!(u.texture!==null&&gc(u.shape,l))?cB(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var hB={kernelName:Go,backendName:"webgl",kernelFunc:Ae},e7=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${_.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},jW=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
+    `}},dB=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1193,12 +1193,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function UW(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Si(e,t,n,r){let a=UW(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new P3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new P3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new jW({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var GW=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=HW(t);this.userCode=`
+    `}};function pB(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Ri(e,t,n,r){let a=pB(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new e7({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new e7({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new dB({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var mB=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=lt(this.rank),a=fB(t);this.userCode=`
     void main() {
       ${r} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function HW(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var qW=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ot(this.rank),a=x3("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function fB(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var yB=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=lt(this.rank),a=O3("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${r} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1215,14 +1215,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function wp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qW(e.shape,t):new GW(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function XW(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=wp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=xe({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=dd(e.dtype),g=Si(A,y,"sum",r),w=xe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),w}function AA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return XW(a,s,i,n)}var KW={kernelName:Zs,backendName:"webgl",kernelFunc:AA};function En(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=fA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=wp(a,s,i);return c}var ZW={kernelName:ti,backendName:"webgl",kernelFunc:En},L3=1e3;function bp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(c>=2&&u>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=xe({inputs:{x:e},backend:a,attrs:{shape:_}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[N,T],F=Math.max(y,g),$=n?N.shape[1]:N.shape[2],L=s!=null,V=i!=null,j=l==="leakyrelu",U=l!=null?xp(l,!0):null,X=L||V||j||U!=null,G;if((p===1||f===1)&&$>L3&&X===!1){let Y=N,ae=T;n&&(Y=En({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(ae=En({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let te=f!==1,ie=f===1,Q=Y;te&&(Q=xe({inputs:{x:Y},backend:a,attrs:{shape:[F,$,1]}}),E.push(Q));let he=f===1?2:1,oe=ae;ie&&(oe=xe({inputs:{x:ae},backend:a,attrs:{shape:[F,1,$]}}),E.push(oe));let fe=z3({inputs:{a:Q,b:oe},backend:a});G=AA({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),E.push(fe)}else{let Y=cr(e.dtype,t.dtype),ae=new F3(_,x,[F,p,f],n,r,L,U,V,j),te=[N,T];if(s!=null&&te.push(s),V&&te.push(i),j){let ie=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=xe({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function YW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return bp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var JW={kernelName:ni,backendName:"webgl",kernelFunc:YW},W3="return abs(x);";function QW(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=g3(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Dl(r.shape,W3):a=new Ha(r.shape,W3),n.runWebGLProgram(a,[r],r.dtype)}var eB={kernelName:no,backendName:"webgl",kernelFunc:QW},tB=Nr+`
+    `}};function kp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yB(e.shape,t):new mB(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function AB(e,t,n,r){let a=t,s=e.shape.length,i=_.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=kp(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=C.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=C.expandShapeToKeepDim(h,i));let f=_.sizeFromShape(d),m=_.sizeFromShape(e.shape)/f,y=Ae({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),A=yd(e.dtype),g=Ri(y,A,"sum",r),x=Ae({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),x}function Ip(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return AB(a,s,i,n)}var gB={kernelName:Qs,backendName:"webgl",kernelFunc:Ip};function fn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=Sy(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=kp(a,s,i);return c}var xB={kernelName:si,backendName:"webgl",kernelFunc:fn},t7=1e3;function Sp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),y=t.shape.slice(0,-2),A=_.sizeFromShape(m),g=_.sizeFromShape(y),x=A===g||A===1||g===1;_.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${y}).`);let v=(A>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);_.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[A,h,p]:[A,p,h],b=r?[g,f,d]:[g,d,f],k=Ae({inputs:{x:e},backend:a,attrs:{shape:w}}),N=Ae({inputs:{x:t},backend:a,attrs:{shape:b}}),E=[k,N],F=Math.max(A,g),O=n?k.shape[1]:k.shape[2],L=s!=null,V=i!=null,j=l==="leakyrelu",U=l!=null?vp(l,!0):null,X=L||V||j||U!=null,G;if((p===1||f===1)&&O>t7&&X===!1){let Y=k,ae=N;n&&(Y=fn({inputs:{x:k},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(ae=fn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let te=f!==1,ie=f===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[F,O,1]}}),E.push(Q));let he=f===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[F,1,O]}}),E.push(oe));let fe=Ty({inputs:{a:Q,b:oe},backend:a});G=Ip({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),E.push(fe)}else{let Y=dr(e.dtype,t.dtype),ae=new Z3(w,b,[F,p,f],n,r,L,U,V,j),te=[k,N];if(s!=null&&te.push(s),V&&te.push(i),j){let ie=a.makeTensorInfo([],"float32",_.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:G},backend:a,attrs:{shape:v}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function wB(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return Sp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var bB={kernelName:ii,backendName:"webgl",kernelFunc:wB},n7="return abs(x);";function _B(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=D3(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,n7):a=new Ga(r.shape,n7),n.runWebGLProgram(a,[r],r.dtype)}var vB={kernelName:so,backendName:"webgl",kernelFunc:_B},kB=Sr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,nB=Xe({opSnippet:tB}),rB={kernelName:ro,backendName:"webgl",kernelFunc:nB},aB=Nr+`
+`,IB=Xe({opSnippet:kB}),SB={kernelName:io,backendName:"webgl",kernelFunc:IB},NB=Sr+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,sB=Xe({opSnippet:aB}),iB={kernelName:ao,backendName:"webgl",kernelFunc:sB},B3="return a + b;",oB=nn({opSnippet:B3,packedOpSnippet:B3,supportsComplex:!0,cpuKernelImpl:RL}),lB={kernelName:Ea,backendName:"webgl",kernelFunc:oB},uB=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,TB=Xe({opSnippet:NB}),EB={kernelName:oo,backendName:"webgl",kernelFunc:TB},r7="return a + b;",CB=tn({opSnippet:r7,packedOpSnippet:r7,supportsComplex:!0,cpuKernelImpl:eW}),RB={kernelName:Ca,backendName:"webgl",kernelFunc:CB},MB=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1230,7 +1230,7 @@ return log(x + sqrt(x * x - 1.0));`,sB=Xe({opSnippet:aB}),iB={kernelName:ao,back
         float result = ${r};
         setOutput(result);
       }
-    `}},cB=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+    `}},FB=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1238,7 +1238,7 @@ return log(x + sqrt(x * x - 1.0));`,sB=Xe({opSnippet:aB}),iB={kernelName:ao,back
         vec4 result = ${r};
         setOutput(result);
       }
-    `}};function _p(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Wn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=_p({inputs:r.slice(0,o),backend:n}),c=_p({inputs:r.slice(o),backend:n});return _p({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>cr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new cB(r[0].shape,s):new uB(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var hB={kernelName:cs,backendName:"webgl",kernelFunc:_p};function dB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("all",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Si(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var pB={kernelName:Ch,backendName:"webgl",kernelFunc:dB};function fB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("any",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Si(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var mB={kernelName:Rh,backendName:"webgl",kernelFunc:fB},AB=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function Np(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Vn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Np({inputs:r.slice(0,o),backend:n}),c=Np({inputs:r.slice(o),backend:n});return Np({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>dr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new FB(r[0].shape,s):new MB(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var $B={kernelName:ds,backendName:"webgl",kernelFunc:Np};function DB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("all",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"all",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var OB={kernelName:lo,backendName:"webgl",kernelFunc:DB};function zB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("any",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"any",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var PB={kernelName:uo,backendName:"webgl",kernelFunc:zB},LB=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1258,14 +1258,14 @@ return log(x + sqrt(x * x - 1.0));`,sB=Xe({opSnippet:aB}),iB={kernelName:ao,back
         }
         setOutput(float(bestIndex));
       }
-    `}},yB=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ot(o),c=pn("coords",o),u,h;if(s===1){h=o+1;let N=ot(h);u=`
-        ${N} sourceLocR = ${N}(${c.join()}, 0);
+    `}},WB=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,_.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=lt(o),c=pn("coords",o),u,h;if(s===1){h=o+1;let k=lt(h);u=`
+        ${k} sourceLocR = ${k}(${c.join()}, 0);
         ++${c[o-1]};
-        ${N} sourceLocG = ${N}(${c.join()}, 0);
+        ${k} sourceLocG = ${k}(${c.join()}, 0);
         ++${c[o-2]};
-        ${N} sourceLocA = ${N}(${c.join()}, 0);
+        ${k} sourceLocA = ${k}(${c.join()}, 0);
         --${c[o-1]};
-        ${N} sourceLocB = ${N}(${c.join()}, 0);
+        ${k} sourceLocB = ${k}(${c.join()}, 0);
         --${c[o-2]};`}else h=o,u=`
         ${l} sourceLocR = coords;
         ++${c[o-1]};
@@ -1274,15 +1274,15 @@ return log(x + sqrt(x * x - 1.0));`,sB=Xe({opSnippet:aB}),iB={kernelName:ao,back
         ${l} sourceLocA = coords;
         --${c[o-1]};
         ${l} sourceLocB = coords;
-        --${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),m=pn("sourceLocR",h-1).concat("inIdx.r"),A=pn("sourceLocG",h-1).concat("inIdx.g"),y=pn("sourceLocB",h-1).concat("inIdx.b"),g=pn("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
+        --${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(k=>"int "+k),m=pn("sourceLocR",h-1).concat("inIdx.r"),y=pn("sourceLocG",h-1).concat("inIdx.g"),A=pn("sourceLocB",h-1).concat("inIdx.b"),g=pn("sourceLocA",h-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=r?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
-                             getBestIndicesAChannel(${A.join()}),
                              getBestIndicesAChannel(${y.join()}),
-                             getBestIndicesAChannel(${g.join()})));`,_=`vec4(
+                             getBestIndicesAChannel(${A.join()}),
+                             getBestIndicesAChannel(${g.join()})));`,w=`vec4(
             getAChannel(${m.join()}),
-            hasNextCol ? getAChannel(${A.join()}) : 0.,
-            hasNextRow ? getAChannel(${y.join()}) : 0.,
-            hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,x=r?"":`
+            hasNextCol ? getAChannel(${y.join()}) : 0.,
+            hasNextRow ? getAChannel(${A.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,b=r?"":`
       float getBestIndicesAChannel(${f.join()}) {
         return getChannel(getBestIndicesA(${d.join()}),
                                           vec2(${d.slice(-2).join()}));
@@ -1291,7 +1291,7 @@ return log(x + sqrt(x * x - 1.0));`,sB=Xe({opSnippet:aB}),iB={kernelName:ao,back
         return getChannel(getA(${d.join()}),
                                vec2(${d.slice(-2).join()}));
       }
-      ${x}
+      ${b}
       void main() {
         ${l} coords = getOutputCoords();
         bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
@@ -1301,15 +1301,15 @@ return log(x + sqrt(x * x - 1.0));`,sB=Xe({opSnippet:aB}),iB={kernelName:ao,back
           sourceLocB${p}, sourceLocA${p}) * ${t};
         ivec4 inIdx = srcIdx;
         vec4 bestIndex = vec4(inIdx);
-        vec4 bestValue = ${_};
+        vec4 bestValue = ${w};
 
         for (int i = 0; i < ${t}; i++) {
           inIdx = srcIdx;
-          ${b}
-          vec4 candidate = ${_};
+          ${v}
+          vec4 candidate = ${w};
           bvec4 nan = isnan(candidate);
           bvec4 replace = bvec4(
-            vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
+            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
 
           bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                            replace.y  ? candidate.y : bestValue.y,
@@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,sB=Xe({opSnippet:aB}),iB={kernelName:ao,back
         }
         setOutput(bestIndex);
       }
-    `}};function V3(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new AB(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=V3(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function j3(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new yB(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=j3(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function U3(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),c=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=V3(e,c,r);s.push(u);let h=xe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return j3(e,t,r)}function gB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=En({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=U3(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var xB={kernelName:hs,backendName:"webgl",kernelFunc:gB};function wB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=En({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=U3(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var bB={kernelName:yu,backendName:"webgl",kernelFunc:wB},_B=Nr+`
+    `}};function a7(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new LB(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=a7(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function s7(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new WB(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=s7(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function i7(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=_.sizeFromShape(o),c=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=a7(e,c,r);s.push(u);let h=Ae({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return s7(e,t,r)}function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=i7(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var VB={kernelName:ps,backendName:"webgl",kernelFunc:BB};function jB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=i7(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var UB={kernelName:vu,backendName:"webgl",kernelFunc:jB},HB=Sr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,vB=Xe({opSnippet:_B}),kB={kernelName:so,backendName:"webgl",kernelFunc:vB},IB=Nr+"return log(x + sqrt(x * x + 1.0));",NB=Xe({opSnippet:IB}),SB={kernelName:io,backendName:"webgl",kernelFunc:NB},TB=Nr+`
+`,GB=Xe({opSnippet:HB}),qB={kernelName:co,backendName:"webgl",kernelFunc:GB},XB=Sr+"return log(x + sqrt(x * x + 1.0));",KB=Xe({opSnippet:XB}),ZB={kernelName:ho,backendName:"webgl",kernelFunc:KB},YB=Sr+`
   return atan(x);
-`,EB=Xe({opSnippet:TB}),CB={kernelName:oo,backendName:"webgl",kernelFunc:EB},RB=PW+`
+`,JB=Xe({opSnippet:YB}),QB={kernelName:po,backendName:"webgl",kernelFunc:JB},eV=oB+`
   return atan(a, b);
-`,MB=`
+`,tV=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+LW+`
+  `+lB+`
   return result;
-`,FB=nn({opSnippet:RB,packedOpSnippet:MB}),DB={kernelName:uo,backendName:"webgl",kernelFunc:FB},$B=Nr+`
+`,nV=tn({opSnippet:eV,packedOpSnippet:tV}),rV={kernelName:mo,backendName:"webgl",kernelFunc:nV},aV=Sr+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelName:lo,backendName:"webgl",kernelFunc:OB},xc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,sV=Xe({opSnippet:aV}),iV={kernelName:fo,backendName:"webgl",kernelFunc:sV},kc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let k=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${p});
 
@@ -1378,16 +1378,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
               // use the current value.
               float currMinMaxValue = mix(
                   value, minMaxValue, minMaxValueFound);
-              if (value ${N} currMinMaxValue) {
+              if (value ${k} currMinMaxValue) {
                 minMaxValue = value;
                 minMaxValueFound = 1.0;
-                minMaxPosition = ${r?a?m:A:`wR * ${h} + wC`};
+                minMaxPosition = ${r?a?m:y:`wR * ${h} + wC`};
               }
             }
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let g="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,_=s%4,x=`
+      `;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,w=s%4,b=`
       if (${f}) {
         avgValue += dot(values, ones);
       } else {
@@ -1396,7 +1396,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
     `;this.userCode=`
       const ivec2 strides = ivec2(${i}, ${o});
       const ivec2 pads = ivec2(${d}, ${p});
-      const float initializationValue = ${y};
+      const float initializationValue = ${A};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float count = 0.0;
@@ -1420,7 +1420,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
         // max/min x(?, ?, d) to get y(yR, yC, d).
         // ? = to be determined
-        vec4 minMaxValue = vec4(${y});
+        vec4 minMaxValue = vec4(${A});
         float avgValue = 0.0;
         count = 0.0;
 
@@ -1432,7 +1432,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
             continue;
           }
 
-          for (int wC = 0; wC < ${b}; wC += 4) {
+          for (int wC = 0; wC < ${v}; wC += 4) {
             int xC = xCCorner + wC * ${c};
 
             vec4 values = vec4(
@@ -1442,11 +1442,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
               getValue(batch, xR, xC + 3 * ${c}, d)
             );
 
-            ${x}
+            ${b}
           }
 
-          int xC = xCCorner + ${b};
-          if (${_===1}) {
+          int xC = xCCorner + ${v};
+          if (${w===1}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               initializationValue,
@@ -1454,8 +1454,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
               initializationValue
             );
 
-            ${x}
-          } else if (${_===2}) {
+            ${b}
+          } else if (${w===2}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               getValue(batch, xR, xC + ${c}, d),
@@ -1463,8 +1463,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
               initializationValue
             );
 
-            ${x}
-          } else if (${_===3}) {
+            ${b}
+          } else if (${w===3}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               getValue(batch, xR, xC + ${c}, d),
@@ -1472,15 +1472,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
               initializationValue
             );
 
-            ${x}
+            ${b}
           }
         }
-        setOutput(${w});
+        setOutput(${x});
       }
-    `}},yA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
+    `}},Ey=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,y=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
-        const ivec3 pads = ivec3(${m}, ${A}, ${y});
+        const ivec3 pads = ivec3(${m}, ${y}, ${A});
 
         void main() {
           ivec5 coords = getOutputCoords();
@@ -1539,17 +1539,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let b="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,T=`
+      `;return}let v="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,k=s%4,N=`
       if (${g}) {
         avgValue += dot(values, ones);
       } else {
-        minMaxValue = ${b}(values, minMaxValue);
+        minMaxValue = ${v}(values, minMaxValue);
       }
     `;this.userCode=`
       const ivec3 strides =
         ivec3(${i}, ${o}, ${l});
-      const ivec3 pads = ivec3(${m}, ${A}, ${y});
-      const float initializationValue = ${w};
+      const ivec3 pads = ivec3(${m}, ${y}, ${A});
+      const float initializationValue = ${x};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float count = 0.0;
@@ -1574,7 +1574,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
         // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
         // ? = to be determined
-        vec4 minMaxValue = vec4(${w});
+        vec4 minMaxValue = vec4(${x});
         float avgValue = 0.0;
         count = 0.0;
 
@@ -1594,7 +1594,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
               continue;
             }
 
-            for (int wC = 0; wC < ${x}; wC += 4) {
+            for (int wC = 0; wC < ${b}; wC += 4) {
               int xC = xCCorner + wC * ${h};
 
               vec4 values = vec4(
@@ -1604,11 +1604,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
                 getValue(batch, xD, xR, xC + 3 * ${h}, ch)
               );
 
-              ${T}
+              ${N}
             }
 
-            int xC = xCCorner + ${x};
-            if (${N===1}) {
+            int xC = xCCorner + ${b};
+            if (${k===1}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 initializationValue,
@@ -1616,8 +1616,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
                 initializationValue
               );
 
-              ${T}
-            } else if (${N===2}) {
+              ${N}
+            } else if (${k===2}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 getValue(batch, xD, xR, xC + ${h}, ch),
@@ -1625,8 +1625,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
                 initializationValue
               );
 
-              ${T}
-            } else if (${N===3}) {
+              ${N}
+            } else if (${k===3}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 getValue(batch, xD, xR, xC + ${h}, ch),
@@ -1634,13 +1634,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
                 initializationValue
               );
 
-              ${T}
+              ${N}
             }
           }
-          setOutput(${_});
+          setOutput(${w});
         }
       }
-    `}};function PB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Tl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:a},backend:n});let h=new xc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var LB={kernelName:ds,backendName:"webgl",kernelFunc:PB};function WB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new yA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var BB={kernelName:gu,backendName:"webgl",kernelFunc:WB},VB=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
+    `}};function oV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Vn({inputs:{x:a},backend:n});let h=new kc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var lV={kernelName:fs,backendName:"webgl",kernelFunc:oV};function uV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new Ey(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var cV={kernelName:ku,backendName:"webgl",kernelFunc:uV},hV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${c}, ${u});
       const float avgMultiplier = float(${h});
 
@@ -1682,9 +1682,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},jB=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
+    `}},dV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,y=1/(t*n*r);this.userCode=`
       const ivec3 pads = ivec3(${p}, ${f}, ${m});
-      const float avgMultiplier = float(${A});
+      const float avgMultiplier = float(${y});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function UB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new jB(d);return n.runWebGLProgram(p,[a],i.dtype)}var HB={kernelName:Fh,backendName:"webgl",kernelFunc:UB};function GB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Tl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=R.computePool2DInfo(i.shape,o,l,1,c),h=new VB(u);return n.runWebGLProgram(h,[a],i.dtype)}var qB={kernelName:Mh,backendName:"webgl",kernelFunc:GB};function XB(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return bp({a,b:s,transposeA:i,transposeB:o,backend:n})}var KB={kernelName:ps,backendName:"webgl",kernelFunc:XB},ZB=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function pV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new dV(d);return n.runWebGLProgram(p,[a],i.dtype)}var fV={kernelName:Oh,backendName:"webgl",kernelFunc:pV};function mV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Fl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=new hV(u);return n.runWebGLProgram(h,[a],i.dtype)}var yV={kernelName:Dh,backendName:"webgl",kernelFunc:mV};function AV(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Sp({a,b:s,transposeA:i,transposeB:o,backend:n})}var gV={kernelName:ms,backendName:"webgl",kernelFunc:AV},xV=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},YB=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},wV=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -1761,7 +1761,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},JB=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new YB(r.shape,a.shape,s.shape,u,h,l):new ZB(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},QB={kernelName:Is,backendName:"webgl",kernelFunc:JB},tV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=`uniform int start[${this.rank}];`,r=eV(this.rank),a,s=e.map((i,o)=>`sourceLoc.${gA[o]} = start[${o}] + coords.${gA[o]};`);a=`
+    `}},bV=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;_.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new wV(r.shape,a.shape,s.shape,u,h,l):new xV(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},_V={kernelName:Ns,backendName:"webgl",kernelFunc:bV},kV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=vV(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Cy[o]} = start[${o}] + coords.${Cy[o]};`);a=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${s.join(`
@@ -1772,7 +1772,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         ${a}
         setOutput(getSource(${r}));
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},gA=["x","y","z","w","u","v"];function eV(e){if(e===1)return"sourceLoc";if(e<=6)return gA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var nV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=pn("coords",this.rank),r=pn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Cy=["x","y","z","w","u","v"];function vV(e){if(e===1)return"sourceLoc";if(e<=6)return Cy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var IV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=pn("coords",this.rank),r=pn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${r[this.rank-1]};
@@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         ${o}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function rV(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=un.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function wc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=un.parseSliceParams(a,s,i);if(un.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=YL(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=un.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nV(l):new tV(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),rV(a,o,l,n)}var aV={kernelName:Ho,backendName:"webgl",kernelFunc:wc},sV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=[],f=xe({inputs:{x:a},backend:n,attrs:{shape:l}}),m=En({inputs:{x:f},backend:n,attrs:{perm:c}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:u}}),y=wc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},iV={kernelName:xu,backendName:"webgl",kernelFunc:sV};function oV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=y3(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var lV={kernelName:Dh,backendName:"webgl",kernelFunc:oV},uV="return float(a != b);",H3=nn({opSnippet:uV,dtype:"bool"}),cV={kernelName:Fo,backendName:"webgl",kernelFunc:H3};function bc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Wn({inputs:{x:a.complexTensorInfos.real},backend:n})}var hV={kernelName:td,backendName:"webgl",kernelFunc:bc},dV="return float(int(x));";function pV(e,t){let n=new Ha(e.shape,dV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function xA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Wn({inputs:{x:a},backend:n});let i=Ct(a.shape),o=xA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Ga({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=bc({inputs:{input:a},backend:n}),o=xA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Wn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return pV(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=H3({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var fV={kernelName:fs,backendName:"webgl",kernelFunc:xA},G3="return ceil(x);",mV=Xe({opSnippet:G3,packedOpSnippet:G3,cpuKernelImpl:FL}),AV={kernelName:ms,backendName:"webgl",kernelFunc:mV},yV=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function SV(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=un.computeFlatOffset(t,_.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function Ic(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=un.parseSliceParams(a,s,i);if(un.assertParamsValid(a,o,l),_.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=wW(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=un.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IV(l):new kV(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),SV(a,o,l,n)}var NV={kernelName:Zo,backendName:"webgl",kernelFunc:Ic},TV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;_.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=[],f=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),m=fn({inputs:{x:f},backend:n,attrs:{perm:c}}),y=Ae({inputs:{x:m},backend:n,attrs:{shape:u}}),A=Ic({inputs:{x:y},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(y),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),A},EV={kernelName:Iu,backendName:"webgl",kernelFunc:TV};function CV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=$3(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var RV={kernelName:zh,backendName:"webgl",kernelFunc:CV},MV="return float(a != b);",o7=tn({opSnippet:MV,dtype:"bool"}),FV={kernelName:Po,backendName:"webgl",kernelFunc:o7};function Sc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Vn({inputs:{x:a.complexTensorInfos.real},backend:n})}var $V={kernelName:sd,backendName:"webgl",kernelFunc:Sc},DV="return float(int(x));";function OV(e,t){let n=new Ga(e.shape,DV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Ry(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Vn({inputs:{x:a},backend:n});let i=Rt(a.shape),o=Ry({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=qa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Sc({inputs:{input:a},backend:n}),o=Ry({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!_.hasEncodingLoss(a.dtype,s)){let i=Vn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return OV(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",_.getTypedArrayFromDType("bool",1)),o=o7({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var zV={kernelName:ys,backendName:"webgl",kernelFunc:Ry},l7="return ceil(x);",PV=Xe({opSnippet:l7,packedOpSnippet:l7,cpuKernelImpl:nW}),LV={kernelName:As,backendName:"webgl",kernelFunc:PV},WV=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},gV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},BV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function xV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new gV(a.shape):o=new yV(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var wV={kernelName:Ca,backendName:"webgl",kernelFunc:xV},bV=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function VV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new BV(a.shape):o=new WV(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var jV={kernelName:Ra,backendName:"webgl",kernelFunc:VV},UV=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function q3(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function _V(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new bV(r.shape),i=[q3(r,a.complexTensorInfos.real),q3(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var vV={kernelName:wu,backendName:"webgl",kernelFunc:_V},kV=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
+    `}};function u7(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function HV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new UV(r.shape),i=[u7(r,a.complexTensorInfos.real),u7(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var GV={kernelName:Su,backendName:"webgl",kernelFunc:HV},qV=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -1851,18 +1851,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         ${n.join(`
         `)}
       }
-    `}},IV=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ot(r),s=pn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
+    `}},XV=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=lt(r),s=pn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
         return getChannel(
             getT0(${u}), vec2(${c.join()}));
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
         if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
           return getChannel(
-            getT${f}(${vp(i,l,m)}),
-            vec2(${vp(c,l,m)}));
+            getT${f}(${Tp(i,l,m)}),
+            vec2(${Tp(c,l,m)}));
         }`}let d=o.length,p=o[o.length-1];h+=`
         return getChannel(
-          getT${d}(${vp(i,l,p)}),
-          vec2(${vp(c,l,p)}));`,this.userCode=`
+          getT${d}(${Tp(i,l,p)}),
+          vec2(${Tp(c,l,p)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${h}
       }
@@ -1888,18 +1888,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(result);
       }
-    `}};function vp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function kp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Wn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var NV={kernelName:Xh,backendName:"webgl",kernelFunc:kp};function zl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>bc({inputs:{input:f},backend:n})),u=e.map(f=>kp({inputs:{input:f},backend:n})),h=zl(c,t,n),d=zl(u,t,n),p=Ga({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=X3(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=DL(h,u,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=zl(e.slice(0,c),t,n),h=zl(e.slice(c),t,n),d=zl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new IV(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=X3(e,t,n),i=new kV(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function X3(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>xe({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function K3(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(c=>c.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>v.sizeFromShape(c.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return R.assertParamsConsistent(l,s),zl(o,s,n)}var SV={kernelName:co,backendName:"webgl",kernelFunc:K3},Z3=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
+    `}};function Tp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Ep(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Vn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var KV={kernelName:Jh,backendName:"webgl",kernelFunc:Ep};function Vl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>Sc({inputs:{input:f},backend:n})),u=e.map(f=>Ep({inputs:{input:f},backend:n})),h=Vl(c,t,n),d=Vl(u,t,n),p=qa({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=c7(e,t,n),h=c.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),d=c[0].shape[0]===1,p=rW(h,u,r,d),f=C.computeOutShape(e.map(y=>y.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Vl(e.slice(0,c),t,n),h=Vl(e.slice(c),t,n),d=Vl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new XV(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=c7(e,t,n),i=new qV(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function c7(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,_.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function h7(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(c=>c.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>_.sizeFromShape(c.shape)>0);if(o.length===1)return Vn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return C.assertParamsConsistent(l,s),Vl(o,s,n)}var ZV={kernelName:yo,backendName:"webgl",kernelFunc:h7},d7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",y=m?1:2,A=m?2:3,g=m?3:1,x="",v="";n&&(r?x=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
-        }`:a?w=`float activation(float a) {
+        }`:a?x=`float activation(float a) {
           float b = getLeakyreluAlphaAtOutCoords();
           ${n}
-        }`:w=`
+        }`:x=`
           float activation(float x) {
             ${n}
           }
-        `,b="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
-      ${w}
+        `,v="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${x}
 
       const ivec2 strides = ivec2(${o}, ${l});
       const ivec2 pads = ivec2(${s}, ${i});
@@ -1910,7 +1910,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         int d2 = coords[${g}];
 
         ivec2 xRCCorner =
-            ivec2(coords[${A}], coords[${y}]) * strides - pads;
+            ivec2(coords[${y}], coords[${A}]) * strides - pads;
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
 
@@ -2018,11 +2018,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
 
         float result = dotProd;
-        ${_}
-        ${b}
+        ${w}
+        ${v}
         setOutput(result);
       }
-    `}},TV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+    `}},YV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${a}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${n}, ${r});
 
@@ -2110,15 +2110,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},EV=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=dn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
-          blockIndex = rc.y + ${_};
-          pos = rc.x + ${b};
+    `}},JV=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=dn(),y=h==="channelsLast",A=y?0:1,g=y?1:2,x="";for(let v=0;v<=1;v++)for(let w=0;w<=1;w++)x+=`
+          blockIndex = rc.y + ${w};
+          pos = rc.x + ${v};
 
           if(blockIndex < ${e[1]} && pos < ${e[0]}) {
             offsetY = int(blockIndex / (${l})) * ${i} - ${p};
             d0 = offsetY + ${u} * (pos / ${f});
 
-            if(d0 < ${t[y]} && d0 >= 0) {
+            if(d0 < ${t[A]} && d0 >= 0) {
 
               offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
               d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
@@ -2127,14 +2127,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
                 ch = int(mod(float(pos), ${a}.));
 
-                if (${A}) {
+                if (${y}) {
                   innerDims = vec2(d1, ch);
-                  result[${b*2+_}] = getChannel(
+                  result[${v*2+w}] = getChannel(
                     getA(d0, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 } else {
                   innerDims = vec2(d0, d1);
-                  result[${b*2+_}] = getChannel(
+                  result[${v*2+w}] = getChannel(
                     getA(ch, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 }
@@ -2150,11 +2150,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
         vec2 innerDims;
 
-        ${w}
+        ${x}
 
         ${m.output} = result;
       }
-    `}};function Y3({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&u>L3,w=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=xe({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=bp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=xe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(pc(c.shape,_.shape),()=>`packed reshape ${c.shape} to ${_.shape} isn't free`);let N=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=bp({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=x,E.shape=n.outShape,A=Wn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function J3({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=xe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new EV(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),E=xe({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let F=a!=null,$=s!=null,L=o==="leakyrelu",V=o?xp(o,!0):null,j=new F3(E.shape,x.shape,[1,A,n.outChannels],g,w,F,V,$,L),U=[E,x];if(a&&U.push(a),$&&U.push(s),L){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));U.push(Y),b.push(Y)}let X=r.runWebGLProgram(j,U,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=xe({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function CV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=Y3({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=J3({x:a,filter:s,convInfo:d,backend:n});else{let m=new Z3(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=xe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var RV={kernelName:As,backendName:"webgl",kernelFunc:CV},MV=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function p7({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,y,A=[],g=(h===1||d===1)&&u>t7,x=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,v,n.inChannels]}}),b=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),k=Sp({a:w,b,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});y=Ae({inputs:{x:k},backend:r,attrs:{shape:n.outShape}}),A.push(w),A.push(b),A.push(k)}else{let v=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},b=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,_.assert(gc(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let k=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(k);let N=Sp({a:w,b:k,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(N.dataId);_.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=b,E.shape=n.outShape,y=Vn({inputs:{x:N},backend:r}),y.shape=n.outShape,A.push(N)}for(let v of A)r.disposeIntermediateTensorInfo(v);return y}function f7({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,y=d*h,A=[m,y],g=!0,x=!1,v=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),b=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,m,_.sizeFromShape(t.shape)/m]}});v.push(w),v.push(b);let k=new JV(A,w.shape,n),N=r.runWebGLProgram(k,[w],"float32"),E=Ae({inputs:{x:N},backend:r,attrs:{shape:[1,A[0],A[1]]}});v.push(N),v.push(E);let F=a!=null,O=s!=null,L=o==="leakyrelu",V=o?vp(o,!0):null,j=new Z3(E.shape,b.shape,[1,y,n.outChannels],g,x,F,V,O,L),U=[E,b];if(a&&U.push(a),O&&U.push(s),L){let Y=r.makeTensorInfo([],"float32",_.createScalarValue(i,"float32"));U.push(Y),v.push(Y)}let X=r.runWebGLProgram(j,U,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:G}});v.push(X);for(let Y of v)r.disposeIntermediateTensorInfo(Y);return ee}function QV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=p7({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=f7({x:a,filter:s,convInfo:d,backend:n});else{let m=new d7(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=Ae({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var ej={kernelName:gs,backendName:"webgl",kernelFunc:QV},tj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2196,7 +2196,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},FV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
+    `}},nj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},DV=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},rj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2291,7 +2291,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},$V=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
+    `}},aj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${o}, ${l}, ${c});
 
       void main() {
@@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function OV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new MV(d);return n.runWebGLProgram(p,[a,s],"float32")}var zV={kernelName:Oh,backendName:"webgl",kernelFunc:OV};function PV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(c),d=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new FV(d);return n.runWebGLProgram(p,[a,s],"float32")}var LV={kernelName:ys,backendName:"webgl",kernelFunc:PV};function WV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new TV(c);return n.runWebGLProgram(u,[a,s],"float32")}var BV={kernelName:bu,backendName:"webgl",kernelFunc:WV};function VV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=R.computeConv3DInfo(a.shape,l,i,1,o),u=new DV(c);return n.runWebGLProgram(u,[a,s],"float32")}var jV={kernelName:zh,backendName:"webgl",kernelFunc:VV};function UV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=R.computeConv3DInfo(l,s.shape,o,1,i),u=new $V(c);return n.runWebGLProgram(u,[a,s],"float32")}var HV={kernelName:Ph,backendName:"webgl",kernelFunc:UV},GV=M3+`
+    `}};function sj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new tj(d);return n.runWebGLProgram(p,[a,s],"float32")}var ij={kernelName:Lh,backendName:"webgl",kernelFunc:sj};function oj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(c),d=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new nj(d);return n.runWebGLProgram(p,[a,s],"float32")}var lj={kernelName:xs,backendName:"webgl",kernelFunc:oj};function uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new YV(c);return n.runWebGLProgram(u,[a,s],"float32")}var cj={kernelName:Nu,backendName:"webgl",kernelFunc:uj};function hj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=C.computeConv3DInfo(a.shape,l,i,1,o),u=new rj(c);return n.runWebGLProgram(u,[a,s],"float32")}var dj={kernelName:Wh,backendName:"webgl",kernelFunc:hj};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=C.computeConv3DInfo(l,s.shape,o,1,i),u=new aj(c);return n.runWebGLProgram(u,[a,s],"float32")}var fj={kernelName:Bh,backendName:"webgl",kernelFunc:pj},mj=K3+`
   return cos(x);
-`,qV=Xe({opSnippet:GV}),XV={kernelName:gs,backendName:"webgl",kernelFunc:qV},KV=`
+`,yj=Xe({opSnippet:mj}),Aj={kernelName:ws,backendName:"webgl",kernelFunc:yj},gj=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,ZV=Xe({opSnippet:KV}),YV={kernelName:ho,backendName:"webgl",kernelFunc:ZV},JV=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,xj=Xe({opSnippet:gj}),wj={kernelName:Ao,backendName:"webgl",kernelFunc:xj},bj=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,y,A]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,x,v]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${g});
       void main() {
@@ -2375,15 +2375,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
           return;
         }
 
-        float height_scale = ${A};
-        float width_scale = ${w};
+        float height_scale = ${y};
+        float width_scale = ${x};
 
-        float in_y = ${y};
+        float in_y = ${A};
         if( in_y < 0.0 || in_y > ${p} ) {
           setOutput(float(${a}));
           return;
         }
-        float in_x = ${b};
+        float in_x = ${v};
         if( in_x < 0.0 || in_x > ${f} ) {
           setOutput(float(${a}));
           return;
@@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
           setOutput(newValue);
         }
       }
-    `}},QV=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new JV(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},ej={kernelName:po,backendName:"webgl",kernelFunc:QV},t7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${Q3(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},_j=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new bj(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},vj={kernelName:go,backendName:"webgl",kernelFunc:_j},A7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${m7(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
-        ${ot(r)} coords = getOutputCoords();
-        int end = ${e7(r,"coords")};
+        ${lt(r)} coords = getOutputCoords();
+        int end = ${y7(r,"coords")};
         float val = ${a};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${e7(r,"coords")} = idx;
-          val += getX(${Q3(r,"coords")});
+          ${y7(r,"coords")} = idx;
+          val += getX(${m7(r,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function Q3(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function e7(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function tj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=R.getAxesPermutation([s],l),u=a;c!=null&&(u=En({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Wn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new t7(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new t7(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=R.getUndoAxesPermutation(c),m=En({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var nj={kernelName:xs,backendName:"webgl",kernelFunc:tj};function rj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=y3(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=ML(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var aj={kernelName:Lh,backendName:"webgl",kernelFunc:rj},sj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function m7(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function y7(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function kj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=C.getAxesPermutation([s],l),u=a;c!=null&&(u=fn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Vn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new A7(u.shape,!1,o),y=m.getCustomSetupFunc(f),A=p;p=n.runWebGLProgram(m,[p],p.dtype,y),n.disposeIntermediateTensorInfo(A)}if(i){let f=new A7(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=C.getUndoAxesPermutation(c),m=fn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var Ij={kernelName:bs,backendName:"webgl",kernelFunc:kj};function Sj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=$3(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=tW(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Nj={kernelName:Vh,backendName:"webgl",kernelFunc:Sj},Tj=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2447,18 +2447,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function ij(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new sj(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var oj={kernelName:fo,backendName:"webgl",kernelFunc:ij},n7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Ej(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new Tj(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var Cj={kernelName:xo,backendName:"webgl",kernelFunc:Ej},g7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,y="",A="";n&&(r?y=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
-        }`:a?A=`float activation(float a) {
+        }`:a?y=`float activation(float a) {
           float b = getLeakyreluAlphaAtOutCoords();
           ${n}
-        }`:A=`
+        }`:y=`
           float activation(float x) {
             ${n}
           }
-        `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
-      ${A}
+        `,A="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${y}
 
       const ivec2 strides = ivec2(${c}, ${u});
       const ivec2 pads = ivec2(${o}, ${l});
@@ -2500,141 +2500,159 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
         float result = dotProd;
         ${g}
-        ${y}
+        ${A}
         setOutput(result);
       }
-    `}},r7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
-          vec4 xTexelR${b}C${_*2} = vec4(0.);
-          vec4 wR${b}C${_} = vec4(0.);
-          vec4 xR${b}C${_} = vec4(0.);`;for(let b=0;b<p;b++)for(let _=0;_<m;_++){let x=_*2;if(A+=`
-          xR = xRCorner + ${b*h};
-          xC = xCCorner + ${x*d};
-        `,u===1){if(x<f&&(l%2==1?A+=`
+    `}},x7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,c=e.padInfo.left,u=e.strideHeight,h=e.strideWidth,d=e.dilationHeight,p=e.dilationWidth,f=e.filterHeight,m=e.filterWidth,y=m,A=`
+      int xR; int xC; int xCOffset;
+      vec4 wTexel; vec4 previous; vec4 final;`;for(let w=0;w<m;w++)A+=`
+          vec4 xTexelC${w*2};
+          vec4 xC${w};`;for(let w=0;w<f;w++){for(let b=0;b<m;b++)A+=`
+          xTexelC${b*2} = vec4(0.0);
+          xC${b} = vec4(0.0);`;A+=`
+        xR = xRCorner + ${w*d};
+        if (xR >=0 && xR < ${i}) {
+      `;for(let b=0;b<y/2+1;b++){let k=b*2;if(A+=`
+          xC = xCCorner + ${k*p};
+          `,h===1){if(k<m&&(c%2==1?(A+=`
                 xCOffset = xC + 1;
-                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
+                if (xCOffset >= 0 && xCOffset < ${o}) {
+                  xTexelC${k} = getX(batch, xR, xCOffset, d1);
 
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${i}) {
-                    xTexelR${b}C${x}.zw = vec2(0.);
+                  if (xCOffset + 1 >= ${o}) {
+                    xTexelC${k}.zw = vec2(0.0);
                   }
-                } else {
-                  xTexelR${b}C${x} = vec4(0.);
                 }
+              `,p===1&&k>0?A+=`
+                xC${k} = vec4(xTexelC${k-2}.zw, xTexelC${k}.xy);
+                `:A+=`
+                  xCOffset = xC + 1 - 2;
 
-                xCOffset = xC + 1 - 2;
-                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
-                  vec4 previous = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset >= 0 && xCOffset < ${o}) {
+                    previous = getX(batch, xR, xCOffset, d1);
 
-                  // Need to manually clear unused channels in case
-                  // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${i}) {
-                    previous.zw = vec2(0.);
-                  }
-
-                  xR${b}C${x} = vec4(previous.zw, xTexelR${b}C${x}.xy);
-                } else {
-                  xR${b}C${x} = vec4(0, 0, xTexelR${b}C${x}.xy);
-                }
-              `:A+=`
-                if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
-                  xTexelR${b}C${x} = getX(batch, xR, xC, d1);
-                } else {
-                  xTexelR${b}C${x} = vec4(0.);
-                }
-
-                xR${b}C${x} = xTexelR${b}C${x};
-              `,x+1<f)){let N=l%2==0?v.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(A+=`
-                  xCOffset = xC + ${l%2} + ${N};
-
-                  if(xR >= 0 && xR < ${s} &&
-                    xCOffset >= 0 && xCOffset < ${i}) {
-                    xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
-                  }
-                `,d>1&&(A+=`
-                    xCOffset -= 2;
-                    if(xR >= 0 && xR < ${s} &&
-                      xCOffset >= 0 && xCOffset < ${i}) {
-                      xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
-                    } else {
-                      xTexelR${b}C${x} = vec4(0.);
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= ${o}) {
+                      previous.zw = vec2(0.0);
                     }
-                  `),A+=`
-                  xR${b}C${x+1} = vec4(
-                    xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.xy);
-                `):A+=`
-                  xCOffset = xC + ${N};
 
-                  if(xR >= 0 && xR < ${s} &&
-                    xCOffset >= 0 && xCOffset < ${i}) {
-                    xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
+                    xC${k} = vec4(previous.zw, xTexelC${k}.xy);
+                  } else {
+                    xC${k} = vec4(0.0, 0.0, xTexelC${k}.xy);
+                  }
+                  `):A+=`
+                if (xC >= 0 && xC < ${o}) {
+                  xTexelC${k} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= ${o}) {
+                    xTexelC${k}.zw = vec2(0.0);
                   }
-
-                  xR${b}C${x+1} = xTexelR${b}C${x+2};
-                `}}else x<f&&(A+=`
-              if(xR >= 0 && xR < ${s}) {
-            `,l%2==1?(A+=`
-                xCOffset = xC + 1 - ${u};
-                if(xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
-                } else {
-                  xTexelR${b}C${x} = vec4(0.);
                 }
 
-                if(xC + 1 >= 0 && xC + 1 < ${i}) {
-                  xTexelR${b}C${x+2} = getX(batch, xR, xC + 1, d1);
-                } else {
-                  xTexelR${b}C${x+2} = vec4(0.);
+                xC${k} = xTexelC${k};
+                `,k+1<m)){let N=c%2==0?_.nearestLargerEven(p):p;p%2==0&&c%2==1||p%2!=0&&c%2!=1?(A+=`
+                  xCOffset = xC + ${c%2} + ${N};
+
+                  if (xCOffset >= 0 && xCOffset < ${o}) {
+                    xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= ${o}) {
+                      xTexelC${k+2}.zw = vec2(0.0);
+                    }
+                  }
+                  `,p>1&&(A+=`
+                    xCOffset -= 2;
+                    if (xCOffset >= 0 && xCOffset < ${o}) {
+                      xTexelC${k} = getX(batch, xR, xCOffset, d1);
+                    }
+                    `),A+=`
+                  xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.xy);
+                  `):N===1?A+=`
+                    xC${k+1} = xTexelC${k};
+                    `:A+=`
+                    xCOffset = xC + ${N};
+
+                    if (xCOffset >= 0 && xCOffset < ${o}) {
+                      xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
+                      if (xCOffset + 1 >= ${o}) {
+                        xTexelC${k+2}.zw = vec2(0.0);
+                      }
+                    }
+
+                    xC${k+1} = xTexelC${k+2};
+                    `}}else k<m&&(c%2==1?(A+=`
+                xCOffset = xC + 1 - ${h};
+                if(xCOffset >= 0 && xCOffset < ${o}) {
+                  xTexelC${k} = getX(batch, xR, xCOffset, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= ${o}) {
+                    xTexelC${k}.zw = vec2(0.0);
+                  }
                 }
 
-                xR${b}C${x} = vec4(
-                  xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
-              `,x+1<f&&(A+=`
-                  vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${u};
-                  if(xCOffset >= 0 && xCOffset < ${i}) {
+                if(xC + 1 >= 0 && xC + 1 < ${o}) {
+                  xTexelC${k+2} = getX(batch, xR, xC + 1, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xC + 2 >= ${o}) {
+                    xTexelC${k+2}.zw = vec2(0.0);
+                  }
+                }
+
+                xC${k} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
+              `,k+1<m&&(A+=`
+                  final = vec4(0.0);
+                  xCOffset = xC + 1 + ${h};
+                  if(xCOffset >= 0 && xCOffset < ${o}) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
-                  xR${b}C${x+1} = vec4(xTexelR${b}C${x+2}.xy, final.xy);
+                  xC${k+1} = vec4(xTexelC${k+2}.xy, final.xy);
                 `)):(A+=`
-                if(xC >= 0 && xC < ${i}) {
-                  xTexelR${b}C${x} = getX(batch, xR, xC, d1);
-                } else {
-                  xTexelR${b}C${x} = vec4(0.);
+                if(xC >= 0 && xC < ${o}) {
+                  xTexelC${k} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= ${o}) {
+                    xTexelC${k}.zw = vec2(0.0);
+                  }
                 }
 
-                xCOffset = xC + ${u};
-                if(xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
-                } else {
-                  xTexelR${b}C${x+2} = vec4(0.);
+                xCOffset = xC + ${h};
+                if(xCOffset >= 0 && xCOffset < ${o}) {
+                  xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset + 1 >= ${o}) {
+                    xTexelC${k+2}.zw = vec2(0.);
+                  }
                 }
 
-                xR${b}C${x} = vec4(
-                  xTexelR${b}C${x}.xy, xTexelR${b}C${x+2}.xy);
-              `,x+1<f&&(A+=`
-                  xR${b}C${x+1} = vec4(
-                    xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
-                `)),A+="}");x<f&&(A+=`
-            vec4 wTexelR${b}C${x} = getW(${b}, ${x}, d1, q);
-            wR${b}C${x} = vec4(wTexelR${b}C${x}.xz, wTexelR${b}C${x}.xz);
-          `,x+1<f&&(A+=`
-              vec4 wTexelR${b}C${x+1} = getW(${b}, ${x+1}, d1, q);
-              wR${b}C${x+1} =
-                vec4(wTexelR${b}C${x+1}.xz, wTexelR${b}C${x+1}.xz);`))}for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`dotProd += xR${b}C${_} * wR${b}C${_};`;let y="",g="";n&&(r?y=`vec4 activation(vec4 a) {
+                xC${k} = vec4(
+                  xTexelC${k}.xy, xTexelC${k+2}.xy);
+              `,k+1<m&&(A+=`
+                  xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
+                `)));k<m&&(A+=`
+            wTexel = getW(${w}, ${k}, d1, q);
+            dotProd += xC${k} * vec4(wTexel.xz, wTexel.xz);
+          `,k+1<m&&(A+=`
+              wTexel = getW(${w}, ${k+1}, d1, q);
+              dotProd += xC${k+1} * vec4(wTexel.xz, wTexel.xz);
+            `))}A+=`
+        }
+      `}let g="",x="";n&&(r?g=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${n}
-        }`:a?y=`vec4 activation(vec4 a) {
+        }`:a?g=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${n}
-        }`:y=`vec4 activation(vec4 x) {
+        }`:g=`vec4 activation(vec4 x) {
           ${n}
-        }`,g="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
-      ${y}
+        }`,x="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${g}
 
-      const ivec2 strides = ivec2(${c}, ${u});
-      const ivec2 pads = ivec2(${o}, ${l});
+      const ivec2 strides = ivec2(${u}, ${h});
+      const ivec2 pads = ivec2(${l}, ${c});
 
       void main() {
 
@@ -2642,21 +2660,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
-        int d1 = d2;
-        int q = 0;
+        int d1 = d2 / ${s};
+        int q = d2 - d1 * ${s};
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
 
-        vec4 dotProd = vec4(0.);
+        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
+        vec4 dotProd = vec4(0.000000000000001);
 
         ${A}
 
-        vec4 result = dotProd;
-        ${w}
-        ${g}
+        vec4 result = dotProd - vec4(0.000000000000001);
+        ${v}
+        ${x}
         setOutput(result);
       }
-    `}};function lj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new r7(h):d=new n7(h),n.runWebGLProgram(d,[a,s],"float32")}var uj={kernelName:ws,backendName:"webgl",kernelFunc:lj},cj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function Rj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),_.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new x7(h):d=new g7(h),n.runWebGLProgram(d,[a,s],"float32")}var Mj={kernelName:_s,backendName:"webgl",kernelFunc:Rj},Fj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2691,7 +2710,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},hj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},$j=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -2736,13 +2755,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function dj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=R.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new cj(h);return n.runWebGLProgram(d,[a,s],"float32")}var pj={kernelName:Wh,backendName:"webgl",kernelFunc:dj};function fj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new hj(h);return n.runWebGLProgram(d,[a,s],"float32")}var mj={kernelName:Bh,backendName:"webgl",kernelFunc:fj},Aj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function Dj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new Fj(h);return n.runWebGLProgram(d,[a,s],"float32")}var Oj={kernelName:jh,backendName:"webgl",kernelFunc:Dj};function zj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new $j(h);return n.runWebGLProgram(d,[a,s],"float32")}var Pj={kernelName:Uh,backendName:"webgl",kernelFunc:zj},Lj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function yj(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=xe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new Aj(s),l=n.runWebGLProgram(o,[i],i.dtype),c=xe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var gj={kernelName:Vh,backendName:"webgl",kernelFunc:yj},xj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
+    `}};function Wj(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=_.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new Lj(s),l=n.runWebGLProgram(o,[i],i.dtype),c=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var Bj={kernelName:Hh,backendName:"webgl",kernelFunc:Wj},Vj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
       const ivec2 pads = ivec2(${u}, ${h});
       const float neg_infinity = -3.4e38;
@@ -2780,7 +2799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function wj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new xj(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=xe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var bj={kernelName:_u,backendName:"webgl",kernelFunc:wj},_j="return (x >= 0.0) ? x : (exp(x) - 1.0);",vj=`
+    `}};function jj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new Vj(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var Uj={kernelName:Tu,backendName:"webgl",kernelFunc:jj};function Hj(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=C.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,f=[];for(let m=0;m<h;++m){for(let y of u[m]){let{permutationIndices:A,expandDims:g}=C.getEinsumPermutation(p,l[y]),x;C.isIdentityPermutation(A)?x=s[y]:(x=fn({inputs:{x:s[y]},backend:n,attrs:{perm:A}}),f.push(x));let v=x.shape.slice();for(let w=0;w<g.length;++w)v.splice(g[w],0,1);_.arraysEqual(x.shape,v)||(x=Ae({inputs:{x},backend:n,attrs:{shape:v}}),f.push(x)),d===null?d=x:(d=Ty({inputs:{a:x,b:d},backend:n}),f.push(d))}m<h-1&&(c[m]>=0&&(d=Ip({inputs:{x:d},backend:n,attrs:{axis:c[m]-(i.length-p),keepDims:!1}}),f.push(d)),p--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var Gj={kernelName:Xh,backendName:"webgl",kernelFunc:Hj},qj="return (x >= 0.0) ? x : (exp(x) - 1.0);",Xj=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2789,27 +2808,27 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,kj=Xe({opSnippet:_j,packedOpSnippet:vj}),Ij={kernelName:mo,backendName:"webgl",kernelFunc:kj},Nj="return (b >= 1.0) ? a : a * (b + 1.0);",Sj=`
+`,Kj=Xe({opSnippet:qj,packedOpSnippet:Xj}),Zj={kernelName:wo,backendName:"webgl",kernelFunc:Kj},Yj="return (b >= 1.0) ? a : a * (b + 1.0);",Jj=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,Tj=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new gc(Sj,r.shape,a.shape):new Ol(Nj,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},Ej={kernelName:Hh,backendName:"webgl",kernelFunc:Tj},Cj=`
+`,Qj=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(Jj,r.shape,a.shape):new Bl(Yj,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},eU={kernelName:Kh,backendName:"webgl",kernelFunc:Qj},tU=`
   return vec4(equal(a, b));
-`,Rj="return float(a == b);",Mj=nn({opSnippet:Rj,packedOpSnippet:Cj,dtype:"bool"}),Fj={kernelName:yo,backendName:"webgl",kernelFunc:Mj},Dj=`
+`,nU="return float(a == b);",rU=tn({opSnippet:nU,packedOpSnippet:tU,dtype:"bool"}),aU={kernelName:_o,backendName:"webgl",kernelFunc:rU},sU=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
-  float p = ${R.ERF_P};
-  float a1 = ${R.ERF_A1};
-  float a2 = ${R.ERF_A2};
-  float a3 = ${R.ERF_A3};
-  float a4 = ${R.ERF_A4};
-  float a5 = ${R.ERF_A5};
+  float p = ${C.ERF_P};
+  float a1 = ${C.ERF_A1};
+  float a2 = ${C.ERF_A2};
+  float a3 = ${C.ERF_A3};
+  float a4 = ${C.ERF_A4};
+  float a5 = ${C.ERF_A5};
 
   float sign = sign(x);
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,$j=Xe({opSnippet:Dj}),Oj={kernelName:Ao,backendName:"webgl",kernelFunc:$j},a7="return exp(x);",s7=Xe({opSnippet:a7,packedOpSnippet:a7,cpuKernelImpl:$L}),zj={kernelName:_s,backendName:"webgl",kernelFunc:s7};function wA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),xe({inputs:{x:s},backend:r,attrs:{shape:o}})}var Pj={kernelName:go,backendName:"webgl",kernelFunc:wA},i7="return exp(x) - 1.0;",Lj=Xe({opSnippet:i7,packedOpSnippet:i7,cpuKernelImpl:OL}),Wj={kernelName:xo,backendName:"webgl",kernelFunc:Lj},o7=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,iU=Xe({opSnippet:sU}),oU={kernelName:bo,backendName:"webgl",kernelFunc:iU},w7="return exp(x);",b7=Xe({opSnippet:w7,packedOpSnippet:w7,cpuKernelImpl:aW}),lU={kernelName:ks,backendName:"webgl",kernelFunc:b7};function My(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(_.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var uU={kernelName:vo,backendName:"webgl",kernelFunc:My},_7="return exp(x) - 1.0;",cU=Xe({opSnippet:_7,packedOpSnippet:_7,cpuKernelImpl:sW}),hU={kernelName:ko,backendName:"webgl",kernelFunc:cU},v7=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2842,13 +2861,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function l7(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new o7("real",l,t),u=new o7("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Ga({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=xe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function Bj(e){let{inputs:t,backend:n}=e,{input:r}=t;return l7(r,!1,n)}var Vj={kernelName:Gh,backendName:"webgl",kernelFunc:Bj},jj=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function k7(e,t,n){let r=n.texData.get(e.dataId),a=_.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new v7("real",l,t),u=new v7("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=Ae({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function dU(e){let{inputs:t,backend:n}=e,{input:r}=t;return k7(r,!1,n)}var pU={kernelName:Zh,backendName:"webgl",kernelFunc:dU},fU=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       uniform float value;
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function bA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new jj(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var Uj={kernelName:vu,backendName:"webgl",kernelFunc:bA},Hj=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Fy(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||_.inferDtype(a),s==="string"){let i=_.getArrayFromDType(s,_.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new fU(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var mU={kernelName:Eu,backendName:"webgl",kernelFunc:Fy},yU=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -2862,7 +2881,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},Gj={kernelName:wo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new Hj(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},u7="return floor(x);",qj=Xe({opSnippet:u7,packedOpSnippet:u7,cpuKernelImpl:zL}),Xj={kernelName:vs,backendName:"webgl",kernelFunc:qj},Kj=`
+    `}},AU={kernelName:Io,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new yU(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},I7="return floor(x);",gU=Xe({opSnippet:I7,packedOpSnippet:I7,cpuKernelImpl:iW}),xU={kernelName:Is,backendName:"webgl",kernelFunc:gU},wU=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2872,7 +2891,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
   } else {
     return NAN;
   }
-`,Zj=`
+`,bU=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2893,7 +2912,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,Yj=nn({opSnippet:Kj,packedOpSnippet:Zj,dtype:"int32"}),Jj={kernelName:ks,backendName:"webgl",kernelFunc:Yj},Qj=class{constructor(e){this.variableNames=["A"];let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
+`,_U=tn({opSnippet:wU,packedOpSnippet:bU,dtype:"int32"}),vU={kernelName:Ss,backendName:"webgl",kernelFunc:_U},kU=class{constructor(e){this.variableNames=["A"];let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2915,7 +2934,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},eU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
+    `}},IU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2949,7 +2968,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
 
         ${t.output} = result;
       }
-    `}},nU={kernelName:od,backendName:"webgl",kernelFunc:tU},Pl;function tU(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(Pl==null&&(Pl=document.createElement("canvas").getContext("2d")),Pl.canvas.width=l,Pl.canvas.height=c,Pl.drawImage(a,0,0,l,c),a=Pl.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=tr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new eU(h):new Qj(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function rU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(u),A=R.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=Y3({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=J3({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?xp(p,!1):null,T=new Z3(A,b,N,_,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push(F),g.push(F)}y=n.runWebGLProgram(T,E,"float32")}let w=xe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var aU={kernelName:ri,backendName:"webgl",kernelFunc:rU};function sU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?xp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(E),f.push(E)}let N;y?N=new r7(A,b,g,_,x):N=new n7(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var iU={kernelName:ai,backendName:"webgl",kernelFunc:sU},oU=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ot(t.length),a=ot(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},NU={kernelName:hd,backendName:"webgl",kernelFunc:SU},jl;function SU(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(jl==null&&(jl=document.createElement("canvas").getContext("2d")),jl.canvas.width=l,jl.canvas.height=c,jl.drawImage(a,0,0,l,c),a=jl.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=rr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new IU(h):new kU(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function TU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(u),y=C.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),A,g=[];if(y.filterHeight===1&&y.filterWidth===1&&y.dilationHeight===1&&y.dilationWidth===1&&y.strideHeight===1&&y.strideWidth===1&&(y.padInfo.type==="SAME"||y.padInfo.type==="VALID"))A=p7({x:a,filter:s,convInfo:y,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)A=f7({x:a,filter:s,convInfo:y,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let v=i!=null,w=o!=null,b=p==="leakyrelu",k=p?vp(p,!1):null,N=new d7(y,v,k,w,b),E=[a,s];if(i&&E.push(i),o&&E.push(o),b){let F=n.makeTensorInfo([],"float32",_.createScalarValue(f,"float32"));E.push(F),g.push(F)}A=n.runWebGLProgram(N,E,"float32")}let x=Ae({inputs:{x:A},backend:n,attrs:{shape:y.outShape}});return g.push(A),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var EU={kernelName:oi,backendName:"webgl",kernelFunc:TU};function CU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),_.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let y=C.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),A=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels==1,g=d?vp(d,A):null,x=[a,s],v=i!=null,w=o!=null,b=d==="leakyrelu";if(v&&x.push(i),w&&x.push(o),b){let E=n.makeTensorInfo([],"float32",_.createScalarValue(p,"float32"));x.push(E),f.push(E)}let k;A?k=new x7(y,v,g,w,b):k=new g7(y,v,g,w,b);let N=n.runWebGLProgram(k,x,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),N}var RU={kernelName:li,backendName:"webgl",kernelFunc:CU},MU=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${r} strides = ${r}(${this.strides});
          void main() {
           ${a} coords = getOutputCoords();
@@ -2960,21 +2979,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function lU(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=R.prepareAndValidate(r,a),h=xe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/c,c]}}),p=new oU(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var uU={kernelName:_o,backendName:"webgl",kernelFunc:lU},hU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),r=cU(e,2);this.userCode=`
+      `}};function FU(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=C.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[_.sizeFromShape(r.shape)/c,c]}}),p=new MU(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var $U={kernelName:No,backendName:"webgl",kernelFunc:FU},OU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=DU(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function cU(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function dU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],c=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=v.sizeFromShape(s.shape),h=[],d=xe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=xe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=PL(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(c.outputShape,b.dtype,b.values)}let m=new hU(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=xe({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var pU={kernelName:bo,backendName:"webgl",kernelFunc:dU},fU="return float(a > b);",mU=`
+    `}};function DU(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function zU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=_.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=_.sizeFromShape(s.shape),h=[],d=Ae({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=Ae({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),x=n.bufferSync(d),v=oW(x,g,f);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let m=new OU(d.shape,f),y=n.runWebGLProgram(m,[d,p],d.dtype);h.push(y);let A=Ae({inputs:{x:y},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),A}var PU={kernelName:So,backendName:"webgl",kernelFunc:zU},LU="return float(a > b);",WU=`
   return vec4(greaterThan(a, b));
-`,AU=nn({opSnippet:fU,packedOpSnippet:mU,cpuKernelImpl:LL,dtype:"bool"}),yU={kernelName:vo,backendName:"webgl",kernelFunc:AU},gU="return float(a >= b);",xU=`
+`,BU=tn({opSnippet:LU,packedOpSnippet:WU,cpuKernelImpl:lW,dtype:"bool"}),VU={kernelName:To,backendName:"webgl",kernelFunc:BU},jU="return float(a >= b);",UU=`
   return vec4(greaterThanEqual(a, b));
-`,wU=nn({opSnippet:gU,packedOpSnippet:xU,dtype:"bool"}),bU={kernelName:Ns,backendName:"webgl",kernelFunc:wU};function _U(e){let{inputs:t,backend:n}=e,{input:r}=t;return l7(r,!0,n)}var vU={kernelName:qh,backendName:"webgl",kernelFunc:_U},kU="return float(!isnan(x) && !isinf(x));",IU=Xe({opSnippet:kU,dtype:"bool"}),NU={kernelName:ko,backendName:"webgl",kernelFunc:IU},SU="return float(isinf(x));",TU=Xe({opSnippet:SU,dtype:"bool"}),EU={kernelName:Io,backendName:"webgl",kernelFunc:TU},CU="return float(isnan(x));",RU=Xe({opSnippet:CU,dtype:"bool"}),MU={kernelName:No,backendName:"webgl",kernelFunc:RU},FU="return float(a < b);",DU=`
+`,HU=tn({opSnippet:jU,packedOpSnippet:UU,dtype:"bool"}),GU={kernelName:Ts,backendName:"webgl",kernelFunc:HU};function qU(e){let{inputs:t,backend:n}=e,{input:r}=t;return k7(r,!0,n)}var XU={kernelName:Yh,backendName:"webgl",kernelFunc:qU},KU="return float(!isnan(x) && !isinf(x));",ZU=Xe({opSnippet:KU,dtype:"bool"}),YU={kernelName:Eo,backendName:"webgl",kernelFunc:ZU},JU="return float(isinf(x));",QU=Xe({opSnippet:JU,dtype:"bool"}),eH={kernelName:Co,backendName:"webgl",kernelFunc:QU},tH="return float(isnan(x));",nH=Xe({opSnippet:tH,dtype:"bool"}),rH={kernelName:Ro,backendName:"webgl",kernelFunc:nH},aH="return float(a < b);",sH=`
   return vec4(lessThan(a, b));
-`,$U=nn({opSnippet:FU,packedOpSnippet:DU,cpuKernelImpl:WL,dtype:"bool"}),OU={kernelName:So,backendName:"webgl",kernelFunc:$U},zU="return float(a <= b);",PU=`
+`,iH=tn({opSnippet:aH,packedOpSnippet:sH,cpuKernelImpl:uW,dtype:"bool"}),oH={kernelName:Mo,backendName:"webgl",kernelFunc:iH},lH="return float(a <= b);",uH=`
   return vec4(lessThanEqual(a, b));
-`,LU=nn({opSnippet:zU,packedOpSnippet:PU,dtype:"bool"}),WU={kernelName:To,backendName:"webgl",kernelFunc:LU};function BU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=BL(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var VU={kernelName:Kh,backendName:"webgl",kernelFunc:BU},jU=`if (x < 0.0) return NAN;
-  return log(x);`,UU=`
+`,cH=tn({opSnippet:lH,packedOpSnippet:uH,dtype:"bool"}),hH={kernelName:Fo,backendName:"webgl",kernelFunc:cH};function dH(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=cW(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var pH={kernelName:Qh,backendName:"webgl",kernelFunc:dH},fH=`if (x < 0.0) return NAN;
+  return log(x);`,mH=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2983,16 +3002,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,HU=Xe({opSnippet:jU,packedOpSnippet:UU,cpuKernelImpl:VL}),GU={kernelName:Es,backendName:"webgl",kernelFunc:HU},qU="return log(1.0 + x);",XU=Xe({opSnippet:qU}),KU={kernelName:Eo,backendName:"webgl",kernelFunc:XU},ZU="return float(a >= 1.0 && b >= 1.0);",YU=`
+`,yH=Xe({opSnippet:fH,packedOpSnippet:mH,cpuKernelImpl:hW}),AH={kernelName:Rs,backendName:"webgl",kernelFunc:yH},gH="return log(1.0 + x);",xH=Xe({opSnippet:gH}),wH={kernelName:$o,backendName:"webgl",kernelFunc:xH},bH="return float(a >= 1.0 && b >= 1.0);",_H=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,JU=nn({opSnippet:ZU,packedOpSnippet:YU,dtype:"bool"}),QU={kernelName:Co,backendName:"webgl",kernelFunc:JU},eH="return float(!(x >= 1.0));",tH=Xe({opSnippet:eH}),nH={kernelName:ku,backendName:"webgl",kernelFunc:tH},rH="return float(a >= 1.0 || b >= 1.0);",aH=`
+`,vH=tn({opSnippet:bH,packedOpSnippet:_H,dtype:"bool"}),kH={kernelName:Do,backendName:"webgl",kernelFunc:vH},IH="return float(!(x >= 1.0));",SH=Xe({opSnippet:IH}),NH={kernelName:Cu,backendName:"webgl",kernelFunc:SH},TH="return float(a >= 1.0 || b >= 1.0);",EH=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,sH=nn({opSnippet:rH,packedOpSnippet:aH,dtype:"bool"}),iH={kernelName:Iu,backendName:"webgl",kernelFunc:sH},oH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,CH=tn({opSnippet:TH,packedOpSnippet:EH,dtype:"bool"}),RH={kernelName:Ru,backendName:"webgl",kernelFunc:CH},MH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3011,7 +3030,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},lH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+    `}},FH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3073,7 +3092,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},uH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new lH(a.shape,s,i,o,l):new oH(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},cH={kernelName:Nu,backendName:"webgl",kernelFunc:uH},hH=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
+    `}},$H=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new FH(a.shape,s,i,o,l):new MH(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},DH={kernelName:Mu,backendName:"webgl",kernelFunc:$H},OH=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3128,14 +3147,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
       }
       setOutput(result);
       }
-    `}},dH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new hH(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},pH={kernelName:Zh,backendName:"webgl",kernelFunc:dH};function fH(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Si(i,e.dtype,"max",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function c7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[u[x]];let b=fA(g,a.shape,a.dtype,u,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=wp(a,u,n);c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("max",c,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=jL(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=fH(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var mH={kernelName:Cs,backendName:"webgl",kernelFunc:c7},AH=S3+`
+    `}},zH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new OH(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},PH={kernelName:ed,backendName:"webgl",kernelFunc:zH};function LH(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ri(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function S7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,x=new Array(o);for(let b=0;b<x.length;b++)x[b]=a.shape[u[b]];let v=Sy(g,a.shape,a.dtype,u,x);p=n.makeTensorInfo(x,a.dtype);let w=n.texData.get(p.dataId);w.values=v}else p=kp(a,u,n);c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("max",c,o);let[f,m]=C.computeOutAndReduceShapes(p.shape,c),y=f;i&&(y=C.expandShapeToKeepDim(f,l));let A;if(d){let g=n.texData.get(p.dataId).values,x=dW(g,_.sizeFromShape(m),y,a.dtype);A=n.makeTensorInfo(y,a.dtype);let v=n.texData.get(A.dataId);v.values=x}else A=LH(p,m,y,n);return h&&n.disposeIntermediateTensorInfo(p),A}var WH={kernelName:Ms,backendName:"webgl",kernelFunc:S7},BH=U3+`
   return max(a, b);
-`,yH=`
+`,VH=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+gp+`
+  `+_p+`
   return result;
-`,gH=nn({opSnippet:AH,packedOpSnippet:yH,cpuKernelImpl:UL}),xH={kernelName:Rs,backendName:"webgl",kernelFunc:gH};function wH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Tl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:a},backend:n});let h=new xc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var bH={kernelName:Ms,backendName:"webgl",kernelFunc:wH};function _H(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new yA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var vH={kernelName:Su,backendName:"webgl",kernelFunc:_H},kH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,jH=tn({opSnippet:BH,packedOpSnippet:VH,cpuKernelImpl:pW}),UH={kernelName:Fs,backendName:"webgl",kernelFunc:jH};function HH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Vn({inputs:{x:a},backend:n});let h=new kc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var GH={kernelName:$s,backendName:"webgl",kernelFunc:HH};function qH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new Ey(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var XH={kernelName:Fu,backendName:"webgl",kernelFunc:qH},KH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3181,7 +3200,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},IH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
+    `}},ZH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
       const ivec3 pads = ivec3(${u}, ${h}, ${d});
 
       void main() {
@@ -3245,14 +3264,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function NH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new yA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new IH(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var SH={kernelName:Jh,backendName:"webgl",kernelFunc:NH};function TH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Tl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new xc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new kH(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var EH={kernelName:Yh,backendName:"webgl",kernelFunc:TH};function CH(e,t,n,r){let a=new xc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new xc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var RH={kernelName:Qh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=R.computePool2DInfo(r.shape,a,s,c,i),[h,d]=CH(r,o,u,l);return[h,d]}};function MH(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Si(i,"float32","mean",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var FH={kernelName:Fs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=r.shape[u[N]];let _=fA(w,r.shape,r.dtype,u,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=wp(r,u,i);p.push(f),c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=MH(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function DH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Si(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var $H={kernelName:Ds,backendName:"webgl",kernelFunc:DH},OH=S3+`
+    `}};function YH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new Ey(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new ZH(d),y=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),y}var JH={kernelName:nd,backendName:"webgl",kernelFunc:YH};function QH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Fl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new kc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),y=new KH(d),A=n.runWebGLProgram(y,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),A}var eG={kernelName:td,backendName:"webgl",kernelFunc:QH};function tG(e,t,n,r){let a=new kc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new kc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var nG={kernelName:rd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;_.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];_.assert(C.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=C.computePool2DInfo(r.shape,a,s,c,i),[h,d]=tG(r,o,u,l);return[h,d]}};function rG(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ri(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var aG={kernelName:Ds,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=_.parseAxisParam(s,r.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let x=i.texData.get(f.dataId).values,v=new Array(o);for(let k=0;k<v.length;k++)v[k]=r.shape[u[k]];let w=Sy(x,r.shape,r.dtype,u,v);f=i.makeTensorInfo(v,r.dtype);let b=i.texData.get(f.dataId);b.values=w}else f=kp(r,u,i);p.push(f),c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("sum",c,o);let[m,y]=C.computeOutAndReduceShapes(f.shape,c),A=m;a&&(A=C.expandShapeToKeepDim(m,l));let g=rG(f,y,A,i);for(let x of p)i.disposeIntermediateTensorInfo(x);return g}};function sG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"min",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var iG={kernelName:Os,backendName:"webgl",kernelFunc:sG},oG=U3+`
   return min(a, b);
-`,zH=`
+`,lG=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+gp+`
+  `+_p+`
   return result;
-`,PH=nn({opSnippet:OH,packedOpSnippet:zH,cpuKernelImpl:HL}),LH={kernelName:$s,backendName:"webgl",kernelFunc:PH},WH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=ot(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
+`,uG=tn({opSnippet:oG,packedOpSnippet:lG,cpuKernelImpl:fW}),cG={kernelName:zs,backendName:"webgl",kernelFunc:uG},hG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=lt(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3281,7 +3300,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         ${a} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},BH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ot(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
+    `}},dG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=lt(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
         ${a} source = rc;
         if (source < start) {
           source = start * 2 - source - ${h};
@@ -3337,13 +3356,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},VH=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new BH(r.shape,a,s):new WH(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},jH={kernelName:Tu,backendName:"webgl",kernelFunc:VH},UH=`if (b == 0.0) return NAN;
-  return mod(a, b);`,HH=`
+    `}},pG=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new dG(r.shape,a,s):new hG(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},fG={kernelName:Ps,backendName:"webgl",kernelFunc:pG},mG=`if (b == 0.0) return NAN;
+  return mod(a, b);`,yG=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+gp+`
+  `+_p+`
   return result;
-`,GH=nn({opSnippet:UH,packedOpSnippet:HH}),qH={kernelName:Ro,backendName:"webgl",kernelFunc:GH},XH=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
+`,AG=tn({opSnippet:mG,packedOpSnippet:yG}),gG={kernelName:Oo,backendName:"webgl",kernelFunc:AG},xG=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3365,11 +3384,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,OB=Xe({opSnippet:$B}),zB={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},KH=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},wG=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,ZH=`
+return a / b;`,bG=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3387,14 +3406,14 @@ return a / b;`,ZH=`
   }
 
   return result;
-`,h7=nn({opSnippet:KH,packedOpSnippet:ZH,checkOutOfBounds:!0}),YH={kernelName:bs,backendName:"webgl",kernelFunc:h7},d7="return a - b;",p7=nn({opSnippet:d7,packedOpSnippet:d7,supportsComplex:!0,cpuKernelImpl:QL}),JH={kernelName:Qs,backendName:"webgl",kernelFunc:p7};function f7(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=c7({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),c=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=p7({inputs:{a,b:c},backend:n}),h=s7({inputs:{x:u},backend:n}),d=AA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=h7({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var QH={kernelName:Ys,backendName:"webgl",kernelFunc:f7};function eG(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:f7({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new XH(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var tG={kernelName:ed,backendName:"webgl",kernelFunc:eG},m7="return -x;";function nG(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=qL(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Dl(r.shape,m7):a=new Ha(r.shape,m7),n.runWebGLProgram(a,[r],r.dtype)}var rG={kernelName:Mo,backendName:"webgl",kernelFunc:nG},aG=Hr.nonMaxSuppressionV3Impl;function sG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=aG(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var iG={kernelName:Do,backendName:"webgl",kernelFunc:sG},oG=Hr.nonMaxSuppressionV4Impl;function lG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=oG(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var uG={kernelName:$o,backendName:"webgl",kernelFunc:lG},cG=Hr.nonMaxSuppressionV5Impl;function hG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=cG(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var dG={kernelName:Oo,backendName:"webgl",kernelFunc:hG},pG=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,N7=tn({opSnippet:wG,packedOpSnippet:bG,checkOutOfBounds:!0}),_G={kernelName:vs,backendName:"webgl",kernelFunc:N7},T7="return a - b;",E7=tn({opSnippet:T7,packedOpSnippet:T7,supportsComplex:!0,cpuKernelImpl:_W}),vG={kernelName:ni,backendName:"webgl",kernelFunc:E7};function C7(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=_.parseAxisParam([s],a.shape),o=S7({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),c=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),u=E7({inputs:{a,b:c},backend:n}),h=b7({inputs:{x:u},backend:n}),d=Ip({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),f=N7({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var kG={kernelName:ei,backendName:"webgl",kernelFunc:C7};function IG(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:C7({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new xG(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var SG={kernelName:ad,backendName:"webgl",kernelFunc:IG},R7="return -x;";function NG(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=yW(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,R7):a=new Ga(r.shape,R7),n.runWebGLProgram(a,[r],r.dtype)}var TG={kernelName:zo,backendName:"webgl",kernelFunc:NG},EG=Hr.nonMaxSuppressionV3Impl;function CG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=EG(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var RG={kernelName:Lo,backendName:"webgl",kernelFunc:CG},MG=Hr.nonMaxSuppressionV4Impl;function FG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=MG(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var $G={kernelName:Wo,backendName:"webgl",kernelFunc:FG},DG=Hr.nonMaxSuppressionV5Impl;function OG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:y,selectedScores:A}=DG(u,h,d,p,f,m);return[n.makeTensorInfo([y.length],"int32",new Int32Array(y)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var zG={kernelName:Bo,backendName:"webgl",kernelFunc:OG},PG=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${r}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},fG=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),c=new pG(l,s,i,o),u=xe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=xe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},mG={kernelName:zs,backendName:"webgl",kernelFunc:fG};function Ip(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=bc({inputs:{input:r},backend:n}),s=Ip({inputs:{x:a},backend:n}),i=kp({inputs:{input:r},backend:n}),o=Ip({inputs:{x:i},backend:n}),l=Ga({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return bA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var AG={kernelName:el,backendName:"webgl",kernelFunc:Ip};function A7(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=bc({inputs:{input:r},backend:n}),s=A7({inputs:{x:a},backend:n}),i=kp({inputs:{input:r},backend:n}),o=Ip({inputs:{x:i},backend:n}),l=Ga({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return bA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var yG={kernelName:zo,backendName:"webgl",kernelFunc:A7};function gG(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return wA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=wA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=K3({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var xG={kernelName:Po,backendName:"webgl",kernelFunc:gG},wG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=ot(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
+    `}},LG=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=_.sizeFromShape(a.shape),c=new PG(l,s,i,o),u=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},WG={kernelName:Ws,backendName:"webgl",kernelFunc:LG};function Cp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Sc({inputs:{input:r},backend:n}),s=Cp({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Fy({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var BG={kernelName:al,backendName:"webgl",kernelFunc:Cp};function M7(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Sc({inputs:{input:r},backend:n}),s=M7({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Fy({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var VG={kernelName:Vo,backendName:"webgl",kernelFunc:M7};function jG(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return My({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=My({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=h7({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var UG={kernelName:jo,backendName:"webgl",kernelFunc:jG},HG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=lt(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
         uniform float value;
@@ -3421,7 +3440,7 @@ return a / b;`,ZH=`
           setOutput(getX(${o}));
         }
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},bG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ot(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},GG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=lt(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
        if(${c}) {
       `,r===1?"":`}
        rc = outputLoc;
@@ -3446,7 +3465,7 @@ return a / b;`,ZH=`
         ${p}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},y7=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bG(a.shape,s,i):new wG(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},_G={kernelName:Ps,backendName:"webgl",kernelFunc:y7},vG=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},F7=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new GG(a.shape,s,i):new HG(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},qG={kernelName:Bs,backendName:"webgl",kernelFunc:F7},XG=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3455,7 +3474,7 @@ return a / b;`,ZH=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,kG=`
+`,KG=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3469,11 +3488,11 @@ return a / b;`,ZH=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+gp+`
+  `+_p+`
   return result;
-`,IG=nn({opSnippet:vG,packedOpSnippet:kG}),NG={kernelName:Ls,backendName:"webgl",kernelFunc:IG};function SG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=v.parseAxisParam(s,a.shape),u=c,h=R.getAxesPermutation(u,o),d=a;h!=null&&(d=En({inputs:{x:a},backend:n,attrs:{perm:h}}),u=R.getInnerMostAxes(u.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=XL(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,u),A=v.sizeFromShape(m),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=dd(a.dtype),w=Si(y,g,"prod",n);p=xe({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,c);p=xe({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var TG={kernelName:Lo,backendName:"webgl",kernelFunc:SG},g7=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=KL(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},EG={kernelName:Eu,backendName:"webgl",kernelFunc:g7},CG="return 1.0 / x;",RG=Xe({opSnippet:CG}),MG={kernelName:Wo,backendName:"webgl",kernelFunc:RG},FG=Nr+`
+`,ZG=tn({opSnippet:XG,packedOpSnippet:KG}),YG={kernelName:Vs,backendName:"webgl",kernelFunc:ZG};function JG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=_.parseAxisParam(s,a.shape),u=c,h=C.getAxesPermutation(u,o),d=a;h!=null&&(d=fn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=C.getInnerMostAxes(u.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:y,outDtype:A}=AW(d.shape,d.dtype,f,u);p=n.makeTensorInfo(y,A,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,u),y=_.sizeFromShape(m),A=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,y]}}),g=yd(a.dtype),x=Ri(A,g,"prod",n);p=Ae({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(i){l.push(p);let f=C.expandShapeToKeepDim(p.shape,c);p=Ae({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var QG={kernelName:Uo,backendName:"webgl",kernelFunc:JG},$7=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=gW(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},eq={kernelName:$u,backendName:"webgl",kernelFunc:$7},tq="return 1.0 / x;",nq=Xe({opSnippet:tq}),rq={kernelName:Ho,backendName:"webgl",kernelFunc:nq},aq=Sr+`
   return (x < 0.0) ? 0.0 : x;
-`,DG=`
+`,sq=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3483,9 +3502,9 @@ return a / b;`,ZH=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,$G=Xe({opSnippet:FG,packedOpSnippet:DG}),OG={kernelName:Bs,backendName:"webgl",kernelFunc:$G},zG=Nr+`
+`,iq=Xe({opSnippet:aq,packedOpSnippet:sq}),oq={kernelName:Us,backendName:"webgl",kernelFunc:iq},lq=Sr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,PG=`
+`,uq=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3495,7 +3514,7 @@ return a / b;`,ZH=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,LG=Xe({opSnippet:zG,packedOpSnippet:PG}),WG={kernelName:js,backendName:"webgl",kernelFunc:LG},BG=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,cq=Xe({opSnippet:lq,packedOpSnippet:uq}),hq={kernelName:Gs,backendName:"webgl",kernelFunc:cq},dq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/u[0]},
           ${c[1]/u[1]});
@@ -3528,7 +3547,7 @@ return a / b;`,ZH=`
 
         setOutput(newValue);
       }
-    `}},VG=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},pq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${c[0]/u[0]},
           ${c[1]/u[1]},
@@ -3605,7 +3624,7 @@ return a / b;`,ZH=`
 
         setOutput(newValue);
       }
-    `}};function jG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new VG(a.shape,l,c,s,i):new BG(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var UG={kernelName:Vs,backendName:"webgl",kernelFunc:jG},HG=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function fq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new pq(a.shape,l,c,s,i):new dq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var mq={kernelName:Hs,backendName:"webgl",kernelFunc:fq},yq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3686,7 +3705,7 @@ return a / b;`,ZH=`
 
         setOutput(accumulator);
       }
-    `}};function GG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new HG(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var qG={kernelName:rd,backendName:"webgl",kernelFunc:GG},XG=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function Aq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new yq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var gq={kernelName:od,backendName:"webgl",kernelFunc:Aq},xq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/u[0]},
           ${c[1]/u[1]});
@@ -3708,7 +3727,7 @@ return a / b;`,ZH=`
 
         setOutput(newValue);
       }
-    `}};function KG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new XG(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var ZG={kernelName:Cu,backendName:"webgl",kernelFunc:KG},YG=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function wq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new xq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var bq={kernelName:Du,backendName:"webgl",kernelFunc:wq},_q=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3778,17 +3797,17 @@ return a / b;`,ZH=`
 
         setOutput(accumulator);
       }
-    `}};function JG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new YG(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var QG={kernelName:nd,backendName:"webgl",kernelFunc:JG},eq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function vq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new _q(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var kq={kernelName:id,backendName:"webgl",kernelFunc:vq},Iq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
         }
-      `;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=ot(n);this.userCode=`
+      `;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=lt(n);this.userCode=`
       void main() {
         ${s} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}},tq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=pn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`
+    `}},Sq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=pn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3816,7 +3835,7 @@ return a / b;`,ZH=`
           }
           setOutput(result);
         }
-    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function nq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Wn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tq(a.shape,o):new eq(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var rq={kernelName:Us,backendName:"webgl",kernelFunc:nq},aq=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
+    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((A,g)=>d(g,p)),m=f.join(","),y=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${y}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Nq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=_.parseAxisParam(s,a.shape);if(i===0)return Vn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Sq(a.shape,o):new Iq(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Tq={kernelName:qs,backendName:"webgl",kernelFunc:Nq},Eq=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         uniform vec4 params;
@@ -3836,7 +3855,7 @@ return a / b;`,ZH=`
           }
           setOutput(outputValue);
         }
-    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},sq={kernelName:tl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new aq(r.shape,s),[c,u]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},iq=`
+    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},Cq={kernelName:sl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Eq(r.shape,s),[c,u]=C.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},Rq=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3851,7 +3870,7 @@ return a / b;`,ZH=`
       return base + 1.0;
     }
   }
-`,oq=Xe({opSnippet:iq}),lq={kernelName:Hs,backendName:"webgl",kernelFunc:oq},uq="return inversesqrt(x);",cq=Xe({opSnippet:uq,cpuKernelImpl:ZL}),hq={kernelName:Gs,backendName:"webgl",kernelFunc:cq},x7=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ot(a.length),l=ot(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
+`,Mq=Xe({opSnippet:Rq}),Fq={kernelName:Xs,backendName:"webgl",kernelFunc:Mq},$q="return inversesqrt(x);",Dq=Xe({opSnippet:$q,cpuKernelImpl:xW}),Oq={kernelName:Ks,backendName:"webgl",kernelFunc:Dq},D7=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(a.length),l=lt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -3871,7 +3890,7 @@ return a / b;`,ZH=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function dq(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=R.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=xe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=xe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new x7(l,o,p.shape.length,f.shape.length,u,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var pq={kernelName:Vo,backendName:"webgl",kernelFunc:dq},fq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=ot(n);this.userCode=`
+      `}};function zq(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=C.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),y=new D7(l,o,p.shape.length,f.shape.length,u,d),A=n.runWebGLProgram(y,[f,p,m],f.dtype),g=Ae({inputs:{x:A},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),g}var Pq={kernelName:qo,backendName:"webgl",kernelFunc:zq},Lq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=lt(n);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${r});
@@ -3881,21 +3900,21 @@ return a / b;`,ZH=`
           setOutput(getB(${a}));
         }
       }
-    `}};function mq(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new fq(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],cr(a.dtype,s.dtype))}var Aq={kernelName:jo,backendName:"webgl",kernelFunc:mq},yq=`
+    `}};function Wq(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new Lq(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],dr(a.dtype,s.dtype))}var Bq={kernelName:Xo,backendName:"webgl",kernelFunc:Wq},Vq=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
-  float scaleAlpha = ${R.SELU_SCALEALPHA};
-  float scale = ${R.SELU_SCALE};
+  float scaleAlpha = ${C.SELU_SCALEALPHA};
+  float scale = ${C.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,gq=Xe({opSnippet:yq}),xq={kernelName:Uo,backendName:"webgl",kernelFunc:gq},wq="return 1.0 / (1.0 + exp(-1.0 * x));",bq=Xe({opSnippet:wq}),_q={kernelName:Xs,backendName:"webgl",kernelFunc:bq},vq=`
+`,jq=Xe({opSnippet:Vq}),Uq={kernelName:Ko,backendName:"webgl",kernelFunc:jq},Hq="return 1.0 / (1.0 + exp(-1.0 * x));",Gq=Xe({opSnippet:Hq}),qq={kernelName:Ys,backendName:"webgl",kernelFunc:Gq},Xq=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,kq=Xe({opSnippet:vq}),Iq={kernelName:qo,backendName:"webgl",kernelFunc:kq},Nq=M3+`
+`,Kq=Xe({opSnippet:Xq}),Zq={kernelName:Jo,backendName:"webgl",kernelFunc:Kq},Yq=K3+`
   return sin(x);
-`,Sq=Xe({opSnippet:Nq}),Tq={kernelName:qs,backendName:"webgl",kernelFunc:Sq},Eq=`
+`,Jq=Xe({opSnippet:Yq}),Qq={kernelName:Zs,backendName:"webgl",kernelFunc:Jq},eX=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,Cq=Xe({opSnippet:Eq}),Rq={kernelName:Go,backendName:"webgl",kernelFunc:Cq},Mq=`
+`,tX=Xe({opSnippet:eX}),nX={kernelName:Yo,backendName:"webgl",kernelFunc:tX},rX=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3915,9 +3934,9 @@ return a / b;`,ZH=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,Fq=Xe({opSnippet:Mq}),Dq={kernelName:Xo,backendName:"webgl",kernelFunc:Fq},$q=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=[],u=y7({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(u.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(u.shape,s,o,!1),f=xe({inputs:{x:u},backend:n,attrs:{shape:h}}),m=En({inputs:{x:f},backend:n,attrs:{perm:d}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},Oq={kernelName:Ru,backendName:"webgl",kernelFunc:$q};function zq(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new x7(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var Pq={kernelName:ad,backendName:"webgl",kernelFunc:zq};function Lq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=wc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var Wq={kernelName:Ko,backendName:"webgl",kernelFunc:Lq},Bq="return sqrt(x);",Vq=Xe({opSnippet:Bq}),jq={kernelName:Ks,backendName:"webgl",kernelFunc:Vq},Uq="return x * x;",Hq=Xe({opSnippet:Uq}),Gq={kernelName:Mu,backendName:"webgl",kernelFunc:Hq},w7="return (a - b) * (a - b);",qq=nn({opSnippet:w7,packedOpSnippet:w7}),Xq={kernelName:Js,backendName:"webgl",kernelFunc:qq};function Kq({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Nr+`
+`,aX=Xe({opSnippet:rX}),sX={kernelName:Qo,backendName:"webgl",kernelFunc:aX},iX=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;_.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,g)=>A*g),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<a.shape.length;++A)l.push([0,0]);let c=[],u=F7({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(u.shape,s,o,!1),d=C.getPermuted(h.length,s.length,!1),p=C.getReshapedPermuted(u.shape,s,o,!1),f=Ae({inputs:{x:u},backend:n,attrs:{shape:h}}),m=fn({inputs:{x:f},backend:n,attrs:{perm:d}}),y=Ae({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},oX={kernelName:Ou,backendName:"webgl",kernelFunc:iX};function lX(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=C.calculateShapes(s,a,o),d=!1,p=new D7(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var uX={kernelName:ld,backendName:"webgl",kernelFunc:lX};function cX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=_.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=Ic({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var hX={kernelName:el,backendName:"webgl",kernelFunc:cX},dX="return sqrt(x);",pX=Xe({opSnippet:dX}),fX={kernelName:Js,backendName:"webgl",kernelFunc:pX},mX="return x * x;",yX=Xe({opSnippet:mX}),AX={kernelName:zu,backendName:"webgl",kernelFunc:yX},O7="return (a - b) * (a - b);",gX=tn({opSnippet:O7,packedOpSnippet:O7}),xX={kernelName:ti,backendName:"webgl",kernelFunc:gX};function wX({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Sr+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,s=new Ha(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var Zq={kernelName:Ma,backendName:"webgl",kernelFunc:Kq},Yq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ot(n.length),s=ot(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
+  `,s=new Ga(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var bX={kernelName:Fa,backendName:"webgl",kernelFunc:wX},_X=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=lt(n.length),s=lt(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
       ${a} strides = ${a}(${t});
 
@@ -3925,15 +3944,15 @@ return a / b;`,ZH=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function Jq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=un.sliceInfo(a.shape,s,i,o,l,c,u,h,d),w=xe({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=wc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=xe({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,N=Be(w.shape,w.dtype,x),T=JL(g,N,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new Yq(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=xe({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var Qq={kernelName:Zo,backendName:"webgl",kernelFunc:Jq},eX="return tan(x);",tX=Xe({opSnippet:eX}),nX={kernelName:Yo,backendName:"webgl",kernelFunc:tX},rX=`
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@@ -4045,7 +4064,7 @@ return a / b;`,ZH=`
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@@ -4151,26 +4170,26 @@ return a / b;`,ZH=`
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Please use 'channelsLast'.`);let O=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(O.dataId).id;return Q7(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,y,A,g,x,F,v,w,b,k,N,E,L),O}var qK={kernelName:_s,backendName:"wasm",setupFunc:HK,kernelFunc:GK},XK=!1,KK=yn(_o,XK,"bool"),ZK=mn(ks);function Dy(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(_.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Nr({inputs:{x:a},backend:r,attrs:{shape:o}})}var YK={kernelName:vo,backendName:"wasm",kernelFunc:Dy};function JK(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var QK={kernelName:Eu,backendName:"wasm",kernelFunc:JK},ev;function eZ(e){ev=e.wasm.cwrap(Io,null,["number","number","number","number","number","number"])}function tZ(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return ev(s,o,l,c,u,i),a}var nZ={kernelName:Io,backendName:"wasm",kernelFunc:tZ,setupFunc:eZ},rZ=mn(Is),aZ=!1,sZ=yn(Ss,aZ),tv;function iZ(e){tv=e.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number"])}function oZ(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(_.sizeFromShape(s.shape)===0)return m;let y=t.dataIdMap.get(m.dataId).id;return tv(u,h,d,p,f,a,y),m}var lZ={kernelName:Ns,backendName:"wasm",setupFunc:iZ,kernelFunc:oZ},nv;function uZ(e){nv=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d),y=Nc[p];if(y==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let A=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,x=m.outChannels,v=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);v=Q.id}let w=m.filterHeight,b=m.filterWidth,k=m.padInfo.top,N=m.padInfo.right,E=m.padInfo.bottom,F=m.padInfo.left,O=m.dilationHeight,L=m.dilationWidth,V=m.strideHeight,j=m.strideWidth,U=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return nv(A,G,ee,Y,g,w,b,v,k,N,E,F,X,O,L,V,j,U,x,y,ie,f||0,te),ae}var hZ={kernelName:oi,backendName:"wasm",setupFunc:uZ,kernelFunc:cZ},rv;function dZ(e){rv=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d,!0),y=Nc[p];if(y==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,x=m.outChannels,v=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);v=Q.id}let w=m.filterHeight,b=m.filterWidth,k=m.padInfo.top,N=m.padInfo.right,E=m.padInfo.bottom,F=m.padInfo.left,O=m.dilationHeight,L=m.dilationWidth,V=m.strideHeight,j=m.strideWidth,U=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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YZ(e){hv=e.wasm.cwrap(Ps,null,["number","array","number","number","array","array","number","number"])}function JZ(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode:a}}=e,s=r.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=r.map(f=>f[0]),h=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(u).buffer),p=new Uint8Array(new Int32Array(h).buffer);return hv(i,c,t.shape.length,Mn[t.dtype],d,p,Oy[a],l),o}var QZ={kernelName:Ps,backendName:"wasm",kernelFunc:JZ,setupFunc:YZ},eY=!0,tY=yn(Ls,eY),nY=mn(zo);function zy(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],a=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var dv;function rY(e){dv=e.wasm.cwrap(Lo,"number",["number","number","number","number","number"])}function 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qt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Rr=class{constructor(e,t,n,r,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=i,this.id=Kv(),s!=null&&(this.originalName=$v(s),this.name=Ov(this.originalName)),this.rank=t.length}},Ane=0,Hp=class{constructor(e,t){this.callArgs=t,this.id=Ane++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},yne=0,qe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=yne++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Aa(n)+"_"+Vp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let a=null;e.batchSize!=null&&(a=e.batchSize),n=[a].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Er(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new B(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Rn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Rn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ma(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!r||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new Di(c),h=Oc(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=Jne(f,a[p]));let m=It(f);t.push(m),p===0?d=f:d=se(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=It(m(r[A],h[A]))}Ut(f),s.push(f)}return d=It(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>z(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Di(s),o=Oc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=It(c(a[l],o[l]));l===0?n=u:n=se(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=It(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return ire(this,e,t,n)}async fitDataset(e,t){return nre(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return _e(s),Rn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=yd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-yd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Aa(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Aa(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=Aa(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)a[s]=Ei(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=kn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await kn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:hre,generatedBy:`TensorFlow.js tfjs-layers v${iy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await kn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=kn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;p6(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){p6(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ya.className="Model";re.registerClass(ya);var N6=class extends ya{};N6.className="Functional";re.registerClass(N6);async function dre(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=$c(n),a=Mr(r,t);if(e.weightsManifest!=null){let s=await kn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),_e(s)}return a}async function fre(e,t){if(t==null&&(t={}),typeof e=="string"){let n=kn.getLoadHandlers(e,t);if(n.length===0)n.push(kn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};xy.className="ThresholdedReLU";re.registerClass(xy);var wy=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new dy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};wy.className="Softmax";re.registerClass(wy);function Gl(e,t,n){if(typeof e=="number")return Ti(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Fr(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function e0(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ka([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function by(e,t){return z(()=>(Et(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function V6(e,t){return z(()=>(Et(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function _re(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=Tr()),Et(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=bd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Zr(o,n)),o})}function j6(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=Tr()),Et(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=by(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ja.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function vre(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=Tr()),Et(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=V6(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=cm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Zr(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var _y=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",_y.verifyArgs(t),this.rank=e,Gt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(t.kernelSize,e,"kernelSize"),this.strides=Gl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,nr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Et(this.dataFormat),this.activation=Ja(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Bt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=Gl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Xr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!CA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ya(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Lc=class extends _y{constructor(e,t){super(e,t);this.kernel=null,Lc.verifyArgs(t),this.filters=t.filters,Gt(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Bt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=Tv(this.activation.getClassName());if(a!=null&&this.rank===2)n=j6(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=_re(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=j6(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=vre(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=lt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Fr(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Nt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Wt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Wc=class extends Lc{constructor(e){super(2,e);Wc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!CA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Wc.className="Conv2D";re.registerClass(Wc);var t0=class extends Lc{constructor(e){super(3,e);t0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};t0.className="Conv3D";re.registerClass(t0);var vy=class extends Wc{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=lt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=e0(o,h,c,this.padding),f=e0(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=_d(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=Zr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=lt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=e0(t[r],o,s,this.padding),t[a]=e0(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};vy.className="Conv2DTranspose";re.registerClass(vy);var U6=class extends Lc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Bt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Bt(t.pointwiseConstraint)}build(e){if(e=lt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new qt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=Tm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Zr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};U6.className="SeparableConv";var ky=class extends U6{constructor(e){super(2,e)}};ky.className="SeparableConv2D";re.registerClass(ky);var n0=class extends Lc{constructor(e){super(1,e);n0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!CA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};n0.className="Conv1D";re.registerClass(n0);var Iy=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Mp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Mp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Mp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Mp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="Cropping2D";re.registerClass(Iy);var Ny=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Et(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Uee(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="UpSampling2D";re.registerClass(Ny);function kre(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Tr()),Et(a);let i=by(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=fl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var Sy=class extends _y{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Bt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=lt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n=kre(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Zr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Fr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Fr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};Sy.className="DepthwiseConv2D";re.registerClass(Sy);function H6(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function G6(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Cr(2,l));if(t=nt(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Qt(a,-1)),a=nt(a,c)),r&&(t=Pn(t,0),a!=null&&(a=Pn(a,0)));let u=[],h,d=n,p=t.shape[0],f=pr(t),m;a!=null&&(m=pr(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=zn(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((E,F)=>w[1][F].mul(_).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&u.push(h)}let A;return o&&(A=cn(u,1)),[h,A,d]})}var Yr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new r0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new qt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Cr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ZA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new $e("Constants support is not implemented in RNN yet.");ZA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new qt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new qt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ma("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Ut(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=H6(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Rr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=G6((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return z(()=>{let t=Ct(e.shape);return t=Te(t,[1,2]),t=Cc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?OA(t,[1,n]):t):this.cell.stateSize>1?[OA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Yr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Mr(r,n);return new e(Object.assign(t,{cell:a}))}};Yr.className="RNN";re.registerClass(Yr);var Fc=class extends qe{},a0=class extends Fc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Gt(this.units,"units"),this.activation=Ja(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Bl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ka([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>zn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>zn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Kr(P(e,s),this.kernel.read()):a=Kr(e,this.kernel.read()),this.bias!=null&&(a=Zr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Kr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ya(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};a0.className="SimpleRNNCell";re.registerClass(a0);var Ty=class extends Yr{constructor(e){e.cell=new a0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};Ty.className="SimpleRNN";re.registerClass(Ty);var s0=class extends Fc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Gt(this.units,"units"),this.activation=Ja(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ja(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Bl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ka([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>zn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>zn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let c=Kr(e,this.kernel.read());this.useBias&&(c=Zr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Pt(u,[2*this.units,this.units],u.rank-1),p=Kr(r,h),[f,m,A]=Pt(c,3,c.rank-1),[y,g]=Pt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let w=Kr(P(o,r),d);l=this.activation.apply(se(A,w));let b=se(P(i,r),P(se(1,kt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ya(this.activation),recurrentActivation:Ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};s0.className="GRUCell";re.registerClass(s0);var Ey=class extends Yr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new s0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Ey.className="GRU";re.registerClass(Ey);var Bc=class extends Fc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Gt(this.units,"units"),this.activation=Ja(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ja(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Bl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ka([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=lt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends Ar{apply(i,o){let l=a.apply([s]),c=new Dp().apply([s]),u=a.apply([s*2]);return Pv(Pv(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>zn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>zn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let h=Kr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,i[0])),h=se(h,Kr(r,this.recurrentKernel.read())),this.useBias&&(h=Zr(h,this.bias.read()));let[d,p,f,m]=Pt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(P(l,a),P(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=P(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ya(this.activation),recurrentActivation:Ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Bc.className="LSTMCell";re.registerClass(Bc);var Cy=class extends Yr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Bc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Cy.className="LSTM";re.registerClass(Cy);var r0=class extends Fc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){ZA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ri(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(Mr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return YA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}JA(t)}};r0.className="StackedRNNCells";re.registerClass(r0);function Qa(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>Wv(t(),n),i=()=>Mc(s,t,r);return!a||a<=1?Ut(i().clone()):Array(a).fill(void 0).map(i).map(o=>Ut(o.clone()))}var Ire=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},q6=class extends Yr{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new qt({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ma("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Ut(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Fr(l,r[0],a,s[0],i[0]),h=Fr(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};q6.className="ConvRNN2D";var i0=class extends Bc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Gt(this.filters,"filters"),this.kernelSize=Gl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Gt(o,"kernelSize")),this.strides=Gl(r||1,2,"strides"),this.strides.forEach(o=>Gt(o,"strides")),this.padding=a||"valid",nr(this.padding),this.dataFormat=s||"channelsLast",Et(this.dataFormat),this.dilationRate=Gl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Gt(o,"dilationRate"))}build(e){var t;e=lt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends Ar{apply(u,h){let d=l.apply([c]),p=jr([c]),f=l.apply([c*2]);return PA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>zn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>zn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=Pt(this.kernel.read(),i,g),[N,T,E,F]=this.useBias?Pt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,N,this.padding),u=this.inputConv(u,b,T,this.padding),h=this.inputConv(h,_,E,this.padding),d=this.inputConv(d,x,F,this.padding);let[$,L,V,j]=Pt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,$),m=this.recurrentConv(m,L),A=this.recurrentConv(A,V),y=this.recurrentConv(y,j);let U=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(P(X,s),P(U,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Ire(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=ua(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Zr(a,n,this.dataFormat):a}recurrentConv(e,t){return ua(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};i0.className="ConvLSTM2DCell";re.registerClass(i0);var Ry=class extends q6{constructor(e){let t=new i0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Ry.className="ConvLSTM2D";re.registerClass(Ry);var o0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Mc(()=>Wv(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};o0.className="Dropout";re.registerClass(o0);var My=class extends o0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};My.className="SpatialDropout1D";re.registerClass(My);var Fy=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Gt(this.units,"units"),this.activation=Ja(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Bt(e.kernelConstraint),this.biasConstraint=Bt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=lt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=lt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=Tv(this.activation.getClassName()),a;return r!=null?a=Kr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Kr(n,this.kernel.read()),this.bias!=null&&(a=Zr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ya(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Fy.className="Dense";re.registerClass(Fy);var Dy=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=lt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Xa(e,1)]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return Zee(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Dy.className="Flatten";re.registerClass(Dy);var $y=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ja(e.activation)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ya(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};$y.className="Activation";re.registerClass($y);var Oy=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return z(()=>(e=Pe(e),Xee(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="RepeatVector";re.registerClass(Oy);var zy=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Xa(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Reshape";re.registerClass(zy);var Py=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Cr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=lt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return nt(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Py.className="Permute";re.registerClass(Py);var Ly=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return Hu(yi(n,this.maskValue),r)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=Hu(yi(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Ly.className="Masking";re.registerClass(Ly);var Wy=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ft(e.inputLength))}this.inputDim=e.inputDim,Gt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Gt(this.outputDim,"outputDim"),this.embeddingsInitializer=gt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=xt(e.embeddingsRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.embeddingsConstraint=Bt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return z(()=>this.maskZero?(e=Pe(e),yi(e,Ue(e))):null)}computeOutputShape(e){if(e=lt(e),this.inputLength==null)return[...e,this.outputDim];let t=ft(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=Ec(n,"int32")),Lv(this.embeddings.read(),n.as1D()).reshape(lt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Wt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="Embedding";re.registerClass(Wy);var Oi=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[lt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Gy.className="Concatenate";re.registerClass(Gy);function Vc(e,t){for(;e<0;)e+=t;return e}function Nre(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new $e("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var qy=class extends Oi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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Xy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Mc(()=>Fp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Xy.className="GaussianNoise";re.registerClass(Xy);var Ky=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Mc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Fp(n.shape,1,r))},()=>n,t.training||!1):n})}};Ky.className="GaussianDropout";re.registerClass(Ky);var Zy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=Cr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Ti(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!v.arraysEqual(c,Cr(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return jc(r,A,y,g,w,this.epsilon)}else return 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a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==qa(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=Rd(n,this.axis,s),l=Ti(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),jc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Nt(this.betaInitializer),gammaInitializer:Nt(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Jy.className="LayerNormalization";re.registerClass(Jy);function Cre(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Tr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=ec(e,t,n,o):i=qu(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function X6(e,t,n,r,a,s){return z(()=>{Et(a),Mv(s),nr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Tr()),s==null&&(s="max"),e=V6(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=bm(e,t,n,o):i=om(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var K6=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Gt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Gt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,nr(this.padding),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){e=lt(e);let t=Fr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=Cc(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Va(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},e2=class extends K6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),nr(r),l0(e,t,n,r,a,"max")}};e2.className="MaxPooling1D";re.registerClass(e2);var t2=class extends K6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return 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t=Fr(t,this.poolSize[0],this.padding,this.strides[0]),n=Fr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},n2=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),nr(r),l0(e,t,n,r,a,"max")}};n2.className="MaxPooling2D";re.registerClass(n2);var r2=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),nr(r),l0(e,t,n,r,a,"avg")}};r2.className="AveragePooling2D";re.registerClass(r2);var Y6=class extends 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t=Fr(t,this.poolSize[0],this.padding,this.strides[0]),n=Fr(n,this.poolSize[1],this.padding,this.strides[1]),r=Fr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},a2=class extends Y6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),nr(r),X6(e,t,n,r,a,"max")}};a2.className="MaxPooling3D";re.registerClass(a2);var s2=class extends Y6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),nr(r),X6(e,t,n,r,a,"avg")}};s2.className="AveragePooling3D";re.registerClass(s2);var J6=class extends qe{constructor(e){super(e);this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Mr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Mre:e.mergeMode,Rre(this.mergeMode),e.weights)throw new $e("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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a=D6(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=D6(s,t);for(let l of i)a.has(l.name)||(n.push(l),a.add(l.name));for(let l in o)r[l]==null&&(r[l]=new Set),o[l].forEach(c=>r[l].add(c))}}return{sorted:n,recipientCounts:Mre(r)}}function Mre(e){let t={};for(let n in e)t[n]=e[n].size;return t}function D6(e,t){let n=new Set,r=[],a={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),r.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let c of o.inputs)a[c.name]==null&&(a[c.name]=new Set),a[c.name].add(o.name),!n.has(c.name)&&s.push(c)}}return{sorted:r,recipientMap:a}}function Rre(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let r=0;r<e.sourceLayer.inboundNodes.length;++r)for(let a of 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e={alphaInitializer:St(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:Wt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};CA.className="PReLU";re.registerClass(CA);var RA=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new De(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return wl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};RA.className="ELU";re.registerClass(RA);var MA=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Pe(e);return n.mul($c(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let 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B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=o4(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=bm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Rr(o,n)),s==="channelsFirst"&&(o=Je(o,[0,4,1,2,3])),o})}var DA=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DA.verifyArgs(t),this.rank=e,Ht(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Zl(t.kernelSize,e,"kernelSize"),this.strides=Zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ar(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=es(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Bt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=Zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Xr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!jy(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Qa(this.activation),useBias:this.useBias,biasInitializer:St(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Uc=class extends DA{constructor(e,t){super(e,t);this.kernel=null,Uc.verifyArgs(t),this.filters=t.filters,Ht(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Bt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. 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Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Bt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Bt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Ft({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Je(e,[0,2,3,1])),n=Wm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Rr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Je(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=St(this.depthwiseInitializer),e.pointwiseInitializer=St(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};u4.className="SeparableConv";var PA=class extends u4{constructor(e){super(2,e)}};PA.className="SeparableConv2D";re.registerClass(PA);var s0=class extends Uc{constructor(e){super(1,e);s0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!jy(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};s0.className="Conv1D";re.registerClass(s0);var LA=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=zp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return zp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=zp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return zp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};LA.className="Cropping2D";re.registerClass(LA);var WA=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Ite(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Je(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return Je(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};WA.className="UpSampling2D";re.registerClass(WA);function iae(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Tr()),Ct(a);let i=$A(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=xl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}var BA=class extends DA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Bt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n=iae(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Rr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=$r(t,this.kernelSize[0],this.padding,this.strides[0]),s=$r(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=St(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};BA.className="DepthwiseConv2D";re.registerClass(BA);function c4(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function h4(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Cr(2,l));if(t=Je(t,c),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Jt(a,-1)),a=Je(a,c)),r&&(t=Wn(t,0),a!=null&&(a=Wn(a,0)));let u=[],h,d=n,p=t.shape[0],f=mr(t),m;a!=null&&(m=mr(a));for(let A=0;A<p;++A){let g=f[A],x=z(()=>e(g,d));if(a==null)h=x[0],d=x[1];else{let v=z(()=>{let w=m[A],b=Ln(w).sub(w),k=x[0].mul(w).add(d[0].mul(b)),N=d.map((E,F)=>x[1][F].mul(w).add(E.mul(b)));return{output:k,newStates:N}});h=v.output,d=v.newStates}o&&u.push(h)}let y;return o&&(y=cn(u,1)),[h,y,d]})}var Zr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new i0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ft({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Cr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){uA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new De("Constants support is not implemented in RNN yet.");uA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Ft({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!_.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ft({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ya("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Rt([n,r])):this.states_=[Rt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Rt([n,r])):this.states_[0]=Rt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!_.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>jt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=c4(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ft({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Mr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=h4((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return z(()=>{let t=Rt(e.shape);return t=Te(t,[1,2]),t=Dc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ky(t,[1,n]):t):this.cell.stateSize>1?[Ky(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Fr(r,n);return new e(Object.assign(t,{cell:a}))}};Zr.className="RNN";re.registerClass(Zr);var Pc=class extends qe{},o0=class extends Pc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Kr(P(e,s),this.kernel.read()):a=Kr(e,this.kernel.read()),this.bias!=null&&(a=Rr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Kr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qa(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};o0.className="SimpleRNNCell";re.registerClass(o0);var VA=class extends Zr{constructor(e){e.cell=new o0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};VA.className="SimpleRNN";re.registerClass(VA);var l0=class extends Pc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let c=Kr(e,this.kernel.read());this.useBias&&(c=Rr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Pt(u,[2*this.units,this.units],u.rank-1),p=Kr(r,h),[f,m,y]=Pt(c,3,c.rank-1),[A,g]=Pt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,A)),o=this.recurrentActivation.apply(se(m,g));let x=Kr(P(o,r),d);l=this.activation.apply(se(y,x));let v=se(P(i,r),P(se(1,kt(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qa(this.activation),recurrentActivation:Qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};l0.className="GRUCell";re.registerClass(l0);var jA=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new l0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};jA.className="GRU";re.registerClass(jA);var qc=class extends Pc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends Ar{apply(i,o){let l=a.apply([s]),c=new Lp().apply([s]),u=a.apply([s*2]);return r6(r6(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let h=Kr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,i[0])),h=se(h,Kr(r,this.recurrentKernel.read())),this.useBias&&(h=Rr(h,this.bias.read()));let[d,p,f,m]=Pt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(P(l,a),P(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let y=P(u,this.activation.apply(c));return[y,y,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qa(this.activation),recurrentActivation:Qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};qc.className="LSTMCell";re.registerClass(qc);var UA=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new qc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};UA.className="LSTM";re.registerClass(UA);var i0=class extends Pc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){uA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Di(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(Fr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return cA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}hA(t)}};i0.className="StackedRNNCells";re.registerClass(i0);function ts(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>s6(t(),n),i=()=>zc(s,t,r);return!a||a<=1?jt(i().clone()):Array(a).fill(void 0).map(i).map(o=>jt(o.clone()))}var oae=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},d4=class extends Zr{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ft({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Rt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ya("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(a)):this.states_=[Rt(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(a)):this.states_[0]=Rt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!_.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=$r(l,r[0],a,s[0],i[0]),h=$r(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};d4.className="ConvRNN2D";var u0=class extends qc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ht(this.filters,"filters"),this.kernelSize=Zl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ht(o,"kernelSize")),this.strides=Zl(r||1,2,"strides"),this.strides.forEach(o=>Ht(o,"strides")),this.padding=a||"valid",ar(this.padding),this.dataFormat=s||"channelsLast",Ct(this.dataFormat),this.dilationRate=Zl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ht(o,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends Ar{apply(u,h){let d=l.apply([c]),p=Pn([c]),f=l.apply([c*2]);return Yy([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),y=l(a,p,2),A=l(a,p,3),g=3,[x,v,w,b]=Pt(this.kernel.read(),i,g),[k,N,E,F]=this.useBias?Pt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,k,this.padding),u=this.inputConv(u,v,N,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,b,F,this.padding);let[O,L,V,j]=Pt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,O),m=this.recurrentConv(m,L),y=this.recurrentConv(y,V),A=this.recurrentConv(A,j);let U=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(P(X,s),P(U,this.activation.apply(se(h,y)))),ee=P(this.recurrentActivation.apply(se(d,A)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=oae(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=ca(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Rr(a,n,this.dataFormat):a}recurrentConv(e,t){return ca(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};u0.className="ConvLSTM2DCell";re.registerClass(u0);var HA=class extends d4{constructor(e){let t=new u0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};HA.className="ConvLSTM2D";re.registerClass(HA);var c0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return zc(()=>s6(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};c0.className="Dropout";re.registerClass(c0);var GA=class extends c0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};GA.className="SpatialDropout1D";re.registerClass(GA);var qA=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Bt(e.kernelConstraint),this.biasConstraint=Bt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=qv(this.activation.getClassName()),a;return r!=null?a=Kr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Kr(n,this.kernel.read()),this.bias!=null&&(a=Rr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Qa(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Dense";re.registerClass(qA);var XA=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[st(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};i2.className="Concatenate";re.registerClass(i2);function Xc(e,t){for(;e<0;)e+=t;return e}function lae(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new De("batchDot is not implemented for tensors of 4D or higher rank yet");if(_.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),_.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var o2=class extends Wi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){_.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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l2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return zc(()=>Pp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};l2.className="GaussianNoise";re.registerClass(l2);var u2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?zc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Pp(n.shape,1,r))},()=>n,t.training||!1):n})}};u2.className="GaussianDropout";re.registerClass(u2);var c2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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qe{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Bt(e.betaConstraint),this.gammaConstraint=Bt(e.gammaConstraint),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new B(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ft({ndim:e.length,axes:{[t]:n}})];let r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=Cr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Mi(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!_.arraysEqual(c,Cr(0,s).slice(0,s-1)),h=()=>{if(u){let y=this.movingMean.read().reshape(l),A=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return Kc(r,y,A,g,x,this.epsilon)}else return 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extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ka(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=Dd(n,this.axis,s),l=Mi(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),Kc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};d2.className="LayerNormalization";re.registerClass(d2);function dae(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Tr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=ac(e,t,n,o):i=Ju(e,t,n,o),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}function p4(e,t,n,r,a,s){return z(()=>{Ct(a),Yv(s),ar(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Tr()),s==null&&(s="max"),e=o4(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Fm(e,t,n,o):i=gm(e,t,n,o),a==="channelsFirst"&&(i=Je(i,[0,4,1,2,3])),i})}var f4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ht(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=$r(t,this.poolSize[0],this.padding,this.strides[0]),n=$r(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},y2=class extends m4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),h0(e,t,n,r,a,"max")}};y2.className="MaxPooling2D";re.registerClass(y2);var A2=class extends m4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),h0(e,t,n,r,a,"avg")}};A2.className="AveragePooling2D";re.registerClass(A2);var y4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),ar(this.padding),this.inputSpec=[new Ft({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=$r(t,this.poolSize[0],this.padding,this.strides[0]),n=$r(n,this.poolSize[1],this.padding,this.strides[1]),r=$r(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},g2=class extends y4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),p4(e,t,n,r,a,"max")}};g2.className="MaxPooling3D";re.registerClass(g2);var x2=class extends y4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),ar(r),p4(e,t,n,r,a,"avg")}};x2.className="AveragePooling3D";re.registerClass(x2);var A4=class extends qe{constructor(e){super(e);this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},w2=class extends A4{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return It(n,1)})}};w2.className="GlobalAveragePooling1D";re.registerClass(w2);var b2=class extends A4{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return Cn(n,1)})}};b2.className="GlobalMaxPooling1D";re.registerClass(b2);var g4=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Ft({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},_2=class extends g4{call(e,t){return z(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};_2.className="GlobalAveragePooling2D";re.registerClass(_2);var v2=class extends g4{call(e,t){return z(()=>{let n=Pe(e);return 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e(s)}},k2=class extends x4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return z(()=>(e=Pe(e),h4((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};k2.className="TimeDistributed";re.registerClass(k2);function pae(e){$i(kte,"BidirectionalMergeMode",e)}var fae="concat",I2=class extends x4{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Fr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Fr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?fae:e.mergeMode,pae(this.mergeMode),e.weights)throw new De("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Fn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=c4(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new 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r=k("size",e,t,n),a=k("dtype",e,t,n),s=k("elementShape",e,t,n),i=k("dynamicSize",e,t,n),o=k("clearAfterRead",e,t,n),l=k("identicalElementShapes",e,t,n),c=k("name",e,t,n),u=new vae(c,a,r,s,l,i,o);return n.addTensorArray(u),[u.idTensor,ge(1)]}case"TensorArrayWriteV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{let r=k("tensorArrayId",e,t,n),a=k("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{let r=k("tensorArrayId",e,t,n),a=k("indices",e,t,n),s=k("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=k("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{let r=k("tensorArrayId",e,t,n),a=k("tensor",e,t,n),s=k("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return[ge(a.size(),"int32")]}case"TensorArrayCloseV3":{let r=k("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{let r=k("tensorListId",e,t,n),a=k("index",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let r=k("indices",e,t,n),a=k("tensor",e,t,n),s=k("elementShape",e,t,n),i=k("numElements",e,t,n),o=Nae(a,r,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let r=k("elementShape",e,t,n),a=k("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,n),o=Iae(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=k("tensorListId",e,t,n),a=k("indices",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=k("tensorListId",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=k("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=k("tensor",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=kae(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=k("tensorListId",e,t,n),a=n.getTensorList(r.id),s=k("dtype",e,t,n),i=k("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=k("tensorListId",e,t,n),a=k("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let 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c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ga(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Fn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Fn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=jn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=jn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=jn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Zae=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Yae="?tfjs-format=file",Jae="model.json",O4=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Zae}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=kn.browserHTTPRequest(e,this.loadOptions);else{let t=kn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(kn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=kn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new N2(S4.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=S4.Instance.transformGraph(e.modelInitializer);this.initializer=new N2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=kn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof We)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Jae}${Yae}`);let n=new O4(e,t);return await n.load(),n}var Qae="3.3.0",z4={};Me(z4,{CSVDataset:()=>L4,Dataset:()=>ql,FileDataSource:()=>W4,TextLineDataset:()=>P4,URLDataSource:()=>B4,array:()=>ese,csv:()=>nse,func:()=>rse,generator:()=>ase,microphone:()=>ise,version_data:()=>ose,webcam:()=>sse,zip:()=>tse});var lse=Qi(Jg()),use=Qi(Jg());function cse(e,t){return d0(e,t)}function d0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Xl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=d0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function hse(e,t=j4){return V4(e,t)}function V4(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Xl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=V4(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function j4(e){return e===null?null:Xl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function U4(e,t){let n=new Map;d0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let s=await a;n.set(r,s)}}return d0(e,t,n)}function Xl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof We))}function pse(e){return e==null||dse(e)||Array.isArray(e)||typeof e=="object"&&e instanceof We||v.isTypedArray(e)}function dse(e){return e===null||typeof e!="object"&&typeof e!="function"}function mse(e){return cse(e,fse)}function fse(e){return e instanceof We?{value:e.clone(),recurse:!1}:Xl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var H4=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},S2=class extends H4{constructor(){super(S2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};S2.INITIAL_CAPACITY=32;function G4(e){return new Ase(e)}function T2(e){return new yse(e)}function gse(e,t){return new q4(e,t)}function wse(e,t=es.FAIL){return new xse(e,t)}var Xt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new Sse(this,e)}filter(e){return new Ise(this,e)}map(e){return new Nse(this,e)}mapAsync(e){return new X4(this,e)}serialMapAsync(e){return new X4(this,e).serial()}flatmap(e){return new Tse(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new kse(this,e,t)}columnMajorBatch(e,t=!0,n=j4){return this.rowMajorBatch(e,t).map(r=>hse(r,n))}concatenate(e,t){return new q4(G4([this,e]),t)}take(e){return e<0||e==null?this:new vse(this,e)}skip(e){return e<0||e==null?this:new _se(this,e)}prefetch(e){return new K4(this,e)}shuffle(e,t){return new Ese(this,e,t)}serial(){return new bse(this)}},Ase=class extends Xt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:mse(e),done:!1}}},yse=class extends Xt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: 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this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},kse=class extends Xt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Ise=class extends Xt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Nse=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Sse=class extends Xt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},X4=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},E2=class extends Xt{constructor(){super();this.outputQueue=new S2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Tse=class extends E2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return!0}},q4=class extends Xt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},es;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(es||(es={}));var xse=class extends Xt{constructor(e,t=es.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof 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-      ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Un(async()=>(await n.iterator()).columnMajorBatch(e,t,Cse),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Un(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Un(async()=>(await t.iterator()).filter(r=>z(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Un(async()=>(await t.iterator()).map(n=>z(()=>e(n))),this.size)}mapAsync(e){let t=this;return Un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Un(async()=>{let r=T2(async()=>({value:await t.iterator(),done:!1}));return gse(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Un(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=lse.alea(t||v.now().toString());return Un(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Un(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};ql.MAX_BUFFER_SIZE=1e4;function Un(e,t=null){return new class extends ql{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function ese(e){return Un(async()=>G4(e),e.length)}function tse(e){if(!Xl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Un(async()=>{let n=await U4(e,r=>{if(r instanceof ql)return{value:r.iterator(),recurse:!1};if(Xl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return wse(n,es.SHORTEST)},t)}function Cse(e){if(e===null)return null;let t=e[0];return pse(t)?{value:Rse(e),recurse:!1}:{value:null,recurse:!0}}function Rse(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof We?cn(e):Ir(e)}var P4=class extends ql{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},p0='"',Gc=Symbol("out"),Z4=Symbol("field"),f0=Symbol("quote"),C2=Symbol("quoteafterquote"),Y4=Symbol("quoteinquote"),L4=class extends ql{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new P4(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Gc;for(let i=0;i<a;i++)switch(s){case Gc:switch(e.charAt(i)){case p0:r=i+1,s=f0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Gc;break;default:s=Z4,r=i;break}break;case Z4:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Gc,r=i+1;break;default:}break;case f0:switch(e.charAt(i)){case p0:s=C2;break;default:}break;case C2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Gc,r=i+1;break;case p0:s=f0;break;default:s=Y4;break}break;case Y4:switch(e.charAt(i)){case p0:s=f0;break;default:}break;default:}if(s===C2?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},J4=class extends Xt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. 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Xse=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Kse=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Zse=[33,133,362,263,1,78,308],ooe=Xse.map(e=>Xc[e]),loe=Kse.map(e=>Xc[e]),uoe=Zse.map(e=>Xc[e]);var Y2=Qr.leftEyeLower0,J2=Qr.rightEyeLower0,Yl={leftBounds:[Y2[0],Y2[Y2.length-1]],rightBounds:[J2[0],J2[J2.length-1]]},C0={count:468,mouth:13,symmetryLine:[13,Qr.midwayBetweenEyes[0]]},m8={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Jl={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function R0(e,t,n,r){for(let a=0;a<Z2.length;a++){let{key:s,indices:i}=Z2[a],o=Qr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var Q2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=qc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?E0(r,[0,0]):T0,l=r!==0?i.map(h=>[...f8(h,o),h[2]]):i,c=r!==0?p8(a):T0,u=[...Kl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+ts(u,c[0]),h[1]+ts(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[Yl.leftBounds[0]][2],r=t[Yl.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=S0(N0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=qc(i),l=ze.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&_r.flags.IS_BROWSER&&(l=ze.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<Jl.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(Jl.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Qr[`${r}EyeUpper0`][Jl.upperCenter]][2],s=t[Qr[`${r}EyeLower0`][Jl.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=c8({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=N0(o),c=S0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&_r.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=C0.count?C0.symmetryLine:m8.symmetryLine;u=K2(i.landmarks[_],i.landmarks[x]);let N=Kl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=ze.rotateWithOffset(t,u,0,T);h=E0(-u,N),n.face.mesh.enabled?c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=T0;let _=t.clone();n.face.mesh.enabled?c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:N}=this.getEyeBox(A,c,Yl.leftBounds[0],Yl.leftBounds[1],!0),{box:T,boxSize:E,crop:F}=this.getEyeBox(A,c,Yl.rightBounds[0],Yl.rightBounds[1]),L=this.irisModel.predict(rt([N,F])).dataSync(),V=L.slice(0,Jl.numCoordinates*3),{rawCoords:j,iris:U}=this.getEyeCoords(V,_,x,!0),X=L.slice(Jl.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,E),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(R0(A,j,"left",null),R0(A,G,"right",null)):Y<1?R0(A,j,"left",["EyeUpper0","EyeLower0"]):R0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,U,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=N0(this.calculateLandmarksBoundingBox(y),1.5);let g=Tn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&_r.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=C0.count?C0.symmetryLine:m8.symmetryLine;u=K2(i.landmarks[_],i.landmarks[x]);let N=Kl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=ze.rotateWithOffset(t,u,0,T);h=E0(-u,N),c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:A},b=S0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var tg=class{constructor(t,n,r,a){this.facePipeline=new Q2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Qr))l[h]=Qr[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxConfidence:Math.round(100*s.boxConfidence)/100,faceConfidence:Math.round(100*s.faceConfidence)/100,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Kt=[null,null,null];async function ng(e){return!Kt[0]&&e.face.enabled||!Kt[1]&&e.face.mesh.enabled||!Kt[2]&&e.face.iris.enabled?(Kt=await Promise.all([!Kt[0]&&e.face.enabled?u8(e):null,!Kt[1]&&e.face.mesh.enabled?ct(pt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Kt[2]&&e.face.iris.enabled?ct(pt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Kt[1]||!Kt[1].modelUrl?le("load model failed:",e.face.mesh.modelPath):e.debug&&le("load model:",Kt[1].modelUrl)),e.face.iris.enabled&&(!Kt[2]||!Kt[1].modelUrl?le("load model failed:",e.face.iris.modelPath):e.debug&&le("load model:",Kt[2].modelUrl))):e.debug&&(le("cached model:",Kt[0].model.modelUrl),le("cached model:",Kt[1].modelUrl),le("cached model:",Kt[2].modelUrl)),new tg(Kt[0],Kt[1],Kt[2],e)}var A8=zi,y8=Xc;var dg={};ir(dg,{PoseNet:()=>pg,load:()=>fg});function Yse(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var rg=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Yse(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function ag(e){return Math.floor(e/2)}var sg=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(ag(t),t);)this.exchange(t,ag(t)),t=ag(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function Jse(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function g8(e,t,n){let[r,a,s]=n.shape,i=new sg(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||Jse(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}var Ql=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],eu=Ql.length,Kc=Ql.reduce((e,t,n)=>(e[t]=n,e),{}),Qse=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Noe=Qse.map(([e,t])=>[Kc[e],Kc[t]]),x8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function ig(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+eu)}}function M0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=ig(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function og(e,t,n){return e<t?t:e>n?n:e}function w8(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function lg(e,t){return{x:e.x+t.x,y:e.y+t.y}}function b8(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function eie(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+eu)}}function tie(e,t){let n=[];for(let r=0;r<eu;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=eie(a,s,r,t);n.push(o),n.push(i)}return Tn(n,[eu,2])}function _8(e,t,n){return z(()=>e.toTensor().mul(ge(t,"int32")).toFloat().add(tie(e,n)))}function nie(e,t){return z(()=>{let n=e.div(ge(t,"int32"));return e.sub(n.mul(ge(t,"int32")))})}function v8(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(ge(n,"int32")).expandDims(1),o=nie(s,n).expandDims(1);return rt([i,o],1)})}var k8=x8.map(([e,t])=>[Kc[e],Kc[t]]),ug=k8.map(([,e])=>e),I8=k8.map(([e])=>e),rie=16;function aie(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function cg(e,t,n,r){return{y:og(Math.round(e.y/t),0,n-1),x:og(Math.round(e.x/t),0,r-1)}}function N8(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=cg(t.position,s,l,c),h=aie(e,u,i),p=lg(t.position,h);for(let A=0;A<o;A++){let y=cg(p,s,l,c),g=ig(y.y,y.x,n,a);p=lg({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=cg(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:Ql[n],score:m}}function S8(e,t,n,r,a,s){let i=t.shape[2],o=ug.length,l=new Array(i),{part:c,score:u}=e,h=M0(c,r,n);l[c.id]={score:u,part:Ql[c.id],position:h};for(let d=o-1;d>=0;--d){let p=ug[d],f=I8[d];l[p]&&!l[f]&&(l[f]=N8(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=I8[d],f=ug[d];l[p]&&!l[f]&&(l[f]=N8(d,l[p],f,t,n,r,a))}return l}async function T8(e,t,n){let r=0,a=v8(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=_8(l,rie,o),u=await c.buffer(),d=Array.from(b8(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:Ql[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var sie=1,E8=16;function C8(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return w8(r,n,i.y,i.x)<=t})}function iie(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(C8(e,t,s,o)||(a+=i),a),0)/n.length}function R8(e,t,n,r,a,s,i){let o=[],l=g8(i,sie,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=M0(u.part,E8,t);if(C8(o,c,h,u.part.id))continue;let d=S8(u,e,t,E8,n,r),p=iie(o,c,d);p>i&&o.push({keypoints:d,score:Math.round(100*p)/100})}return o}async function M8(e){return Promise.all(e.map(t=>t.buffer()))}function 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r=F8(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await uie(t,a,n,this.inputSize):await lie(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function fg(e){Pi?e.debug&&le("cached model:",Pi.modelUrl):(Pi=await ct(pt(e.modelBasePath,e.body.modelPath)),!Pi||!Pi.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",Pi.modelUrl));let t=new rg(Pi);return new pg(t)}var xg={};ir(xg,{HandPose:()=>bg,load:()=>_g});function F0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Zc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function D8(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return 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n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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j8=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],U8=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var An;async function kg(e){return An?e.debug&&le("cached model:",An.modelUrl):(An=await ct(pt(e.modelBasePath,e.body.modelPath)),An.width=parseInt(An.signature.inputs["input_1:0"].tensorShape.dim[2].size),An.height=parseInt(An.signature.inputs["input_1:0"].tensorShape.dim[1].size),!An||!An.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",An.modelUrl)),An}async function Ig(e,t){if(!An||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=ze.resizeBilinear(e,[An.width,An.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let u=await sn(()=>An.predict(a));s=u.result.find(h=>h.size===195||h.size===155).dataSync(),u.result.forEach(h=>h.dispose()),mn("blazepose",u)}else{let u=await An.predict(a);s=u.find(h=>h.size===195||h.size===155).dataSync(),u.forEach(h=>h.dispose())}a.dispose();let i=[],o=s.length===195?j8:U8,l=5;for(let 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r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=ose(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),a=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=ise(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),a=n.getTensorList(r.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),a=I("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let 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c=I("strides",e,t,n),u=f0(e,t,n),h=I("dataFormat",e,t,n).toUpperCase(),d=I("dilations",e,t,n),[p,f]=I("args",e,t,n),m=I("leakyreluAlpha",e,t,n);return{stride:c,pad:u,dataFormat:h,dilations:d,biasArg:p,preluArg:f,activationFunc:a,leakyreluAlpha:m}}var hse=(e,t,n)=>{switch(e.op){case"Conv1D":{let r=I("stride",e,t,n),a=I("pad",e,t,n),s=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Id(I("x",e,t,n),I("filter",e,t,n),r,a,s,i)]}case"Conv2D":{let 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function e8(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Un(d)[0]),u=[];r!=null&&(u=r.map(d=>Un(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((Q4(d)||Sse(d)||Nse(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Tse(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>Un(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return c}var Ese=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Cse=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Rse=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Q4(e){return Ese.indexOf(e.op)>=0}function Sse(e){return Cse.indexOf(e.op)>=0}function Nse(e){return Rse.indexOf(e.op)>=0}var W2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new W2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=e8(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Tse(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(u=>this.graph.nodes[Un(u)[0]]),a=t.map(u=>Un(u)[0]),s=a.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return z(()=>{let u=new J4(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,y]=Un(f),A=[];A[y]=e[f],h[m]=A});let d=this.getFrozenTensorIds(h),p={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let y=Y4(m,h,u,this._resourceManager);if(_.isPromise(y))throw new Error(`The execution of the op '${m.op}' returned a promise. 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You can use model.execute() instead.");let A=o.filter(g=>!Q4(g)&&!An(g.name,p,t)).map(g=>g.name);if(A.length>0){let g="";throw u!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${a}]. Consider providing the following inputs: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=xa(u.node.name,n)),r[u.node.name]==null){let d=Y4(u.node,r,n,this._resourceManager);h||([h]=xa(u.node.name,n));let p=n.currentContext;_.isPromise(d)?c.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=xa(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!An(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!An(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Un(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);_.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&_.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Un(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Un(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Mse=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Fse="?tfjs-format=file",$se="model.json",t8=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Mse}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Nn.browserHTTPRequest(e,this.loadOptions);else{let t=Nn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Nn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=Nn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new W2(G4.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=G4.Instance.transformGraph(e.modelInitializer);this.initializer=new W2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Nn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof ze)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${$se}${Fse}`);let n=new t8(e,t);return await n.load(),n}var Dse="3.4.0",n8={};Me(n8,{CSVDataset:()=>a8,Dataset:()=>Yl,FileDataSource:()=>s8,TextLineDataset:()=>r8,URLDataSource:()=>i8,array:()=>Ose,csv:()=>Pse,func:()=>Lse,generator:()=>Wse,microphone:()=>Vse,version_data:()=>jse,webcam:()=>Bse,zip:()=>zse});var Use=no(d5()),Hse=no(d5());function Gse(e,t){return m0(e,t)}function m0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Jl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=m0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function qse(e,t=l8){return o8(e,t)}function o8(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Jl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=o8(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function l8(e){return e===null?null:Jl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function u8(e,t){let n=new Map;m0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(_.isPromise(a)){let s=await a;n.set(r,s)}}return m0(e,t,n)}function Jl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof ze))}function Kse(e){return e==null||Xse(e)||Array.isArray(e)||typeof e=="object"&&e instanceof ze||_.isTypedArray(e)}function Xse(e){return e===null||typeof e!="object"&&typeof e!="function"}function Yse(e){return Gse(e,Zse)}function Zse(e){return e instanceof ze?{value:e.clone(),recurse:!1}:Jl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var c8=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},B2=class extends c8{constructor(){super(B2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};B2.INITIAL_CAPACITY=32;function h8(e){return new Jse(e)}function V2(e){return new Qse(e)}function eie(e,t){return new d8(e,t)}function nie(e,t=ns.FAIL){return new tie(e,t)}var Gt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new uie(this,e)}filter(e){return new oie(this,e)}map(e){return new lie(this,e)}mapAsync(e){return new p8(this,e)}serialMapAsync(e){return new p8(this,e).serial()}flatmap(e){return new cie(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new iie(this,e,t)}columnMajorBatch(e,t=!0,n=l8){return this.rowMajorBatch(e,t).map(r=>qse(r,n))}concatenate(e,t){return new d8(h8([this,e]),t)}take(e){return e<0||e==null?this:new sie(this,e)}skip(e){return e<0||e==null?this:new aie(this,e)}prefetch(e){return new f8(this,e)}shuffle(e,t){return new hie(this,e,t)}serial(){return new rie(this)}},Jse=class extends Gt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Yse(e),done:!1}}},Qse=class extends Gt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},rie=class extends Gt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},aie=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;_e(e.value)}return this.upstream.next()}},sie=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},iie=class extends Gt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},oie=class extends Gt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},lie=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},uie=class extends Gt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},p8=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},j2=class extends Gt{constructor(){super();this.outputQueue=new B2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},cie=class extends j2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return!0}},d8=class extends Gt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ns;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ns||(ns={}));var tie=class extends Gt{constructor(e,t=ns.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Gt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await u8(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ns.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ns.SHORTEST:return{value:null,done:!0};case ns.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},f8=class extends Gt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new c8(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},hie=class extends f8{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Hse.alea(n||_.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Yl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;_.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Hn(async()=>(await n.iterator()).columnMajorBatch(e,t,die),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Hn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Hn(async()=>(await t.iterator()).filter(r=>z(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Hn(async()=>(await t.iterator()).map(n=>z(()=>e(n))),this.size)}mapAsync(e){let t=this;return Hn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Hn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Hn(async()=>{let r=V2(async()=>({value:await t.iterator(),done:!1}));return eie(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Hn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Use.alea(t||_.now().toString());return Hn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Hn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Yl.MAX_BUFFER_SIZE=1e4;function Hn(e,t=null){return new class extends Yl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function Ose(e){return Hn(async()=>h8(e),e.length)}function zse(e){if(!Jl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Hn(async()=>{let n=await u8(e,r=>{if(r instanceof Yl)return{value:r.iterator(),recurse:!1};if(Jl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return nie(n,ns.SHORTEST)},t)}function die(e){if(e===null)return null;let t=e[0];return Kse(t)?{value:pie(e),recurse:!1}:{value:null,recurse:!0}}function pie(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof ze?cn(e):Ir(e)}var r8=class extends Yl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},y0='"',Jc=Symbol("out"),m8=Symbol("field"),A0=Symbol("quote"),U2=Symbol("quoteafterquote"),y8=Symbol("quoteinquote"),a8=class extends Yl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new r8(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(_.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&_.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(_.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Jc;for(let i=0;i<a;i++)switch(s){case Jc:switch(e.charAt(i)){case y0:r=i+1,s=A0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Jc;break;default:s=m8,r=i;break}break;case m8:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Jc,r=i+1;break;default:}break;case A0:switch(e.charAt(i)){case y0:s=U2;break;default:}break;case U2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Jc,r=i+1;break;case y0:s=A0;break;default:s=y8;break}break;case y8:switch(e.charAt(i)){case y0:s=A0;break;default:}break;default:}if(s===U2?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A8=class extends Gt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new A8(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(_.sizeFromShape(t));return n.set(e,n.length-e.length),Ir(n,t)}},g8=class extends Gt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=sn([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=Rn([s,a,o,i],[1,4])}else this.cropBox=Rn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new g8(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&_.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=pi.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=Jt(ge(e,"float32"),0),n;n=Le.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},x8=class{},w8=class extends Gt{split(e){return new fie(this,e)}},fie=class extends w8{constructor(e,t){super();this.upstream=e,this.impl=new mie(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},mie=class extends j2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Aie=class extends Gt{decodeUTF8(){return new yie(this)}},yie=class extends w8{constructor(e){super();this.upstream=e,this.impl=new gie(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},gie=class extends j2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=$9();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},b8=class extends Aie{constructor(e,t={}){super();this.file=e,this.options=t,_.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function wie(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=xie(e));let a=await _.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new b8(s,t)}else throw new Error(a.statusText)}var xie=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function _8(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var s8=class extends x8{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(_8(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new b8(this.input,this.options)}},i8=class extends x8{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return _8(this.url)?new s8(this.url,this.fileOptions).iterator():wie(this.url,this.fileOptions)}};function Pse(e,t={}){return new a8(new i8(e),t)}function Lse(e){let t=V2(e);return Hn(async()=>t)}function Wse(e){return Hn(async()=>{let t=await e();return V2(()=>t.next())})}async function Bse(e,t){return g8.create(e,t)}async function Vse(e){return A8.create(e)}var jse="3.4.0",bie={tfjs:(bf==null?void 0:bf.version)||void 0,"tfjs-core":(_f==null?void 0:_f.version)||void 0,"tfjs-data":(vf==null?void 0:vf.version)||void 0,"tfjs-layers":(kf==null?void 0:kf.version)||void 0,"tfjs-converter":(If==null?void 0:If.version)||void 0,"tfjs-backend-cpu":w_||void 0,"tfjs-backend-webgl":V3||void 0,"tfjs-backend-wasm":$v||void 0};var 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Cie=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Rie=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Mie=[33,133,362,263,1,78,308],joe=Cie.map(e=>eh[e]),Uoe=Rie.map(e=>eh[e]),Hoe=Mie.map(e=>eh[e]);var hg=Qr.leftEyeLower0,dg=Qr.rightEyeLower0,tu={leftBounds:[hg[0],hg[hg.length-1]],rightBounds:[dg[0],dg[dg.length-1]]},F0={count:468,mouth:13,symmetryLine:[13,Qr.midwayBetweenEyes[0]]},$8={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},nu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function $0(e,t,n,r){for(let a=0;a<cg.length;a++){let{key:s,indices:i}=cg[a],o=Qr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var pg=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Qc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?M0(r,[0,0]):R0,l=r!==0?i.map(h=>[...F8(h,o),h[2]]):i,c=r!==0?M8(a):R0,u=[...Ql({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+rs(u,c[0]),h[1]+rs(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[tu.leftBounds[0]][2],r=t[tu.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=C0(E0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Qc(i),l=Le.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&_r.flags.IS_BROWSER&&(l=Le.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<nu.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(nu.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Qr[`${r}EyeUpper0`][nu.upperCenter]][2],s=t[Qr[`${r}EyeLower0`][nu.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=E8({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=E0(o),c=C0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&_r.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=F0.count?F0.symmetryLine:$8.symmetryLine;u=ug(i.landmarks[w],i.landmarks[b]);let k=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],E=Le.rotateWithOffset(t,u,0,N);h=M0(-u,k),n.face.mesh.enabled?c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=R0;let w=t.clone();n.face.mesh.enabled?c=eu({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshSize,this.meshSize]).div(255):c=eu({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let y=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:w,boxSize:b,crop:k}=this.getEyeBox(y,c,tu.leftBounds[0],tu.leftBounds[1],!0),{box:N,boxSize:E,crop:F}=this.getEyeBox(y,c,tu.rightBounds[0],tu.rightBounds[1]),L=this.irisModel.predict(rt([k,F])).dataSync(),V=L.slice(0,nu.numCoordinates*3),{rawCoords:j,iris:U}=this.getEyeCoords(V,w,b,!0),X=L.slice(nu.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,N,E),Y=this.getLeftToRightEyeDepthDifference(y);Math.abs(Y)<30?($0(y,j,"left",null),$0(y,G,"right",null)):Y<1?$0(y,j,"left",["EyeUpper0","EyeLower0"]):$0(y,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(y,U,"left"),te=this.getAdjustedIrisCoords(y,ee,"right");y=y.concat(ae).concat(te)}let A=this.transformRawCoords(y,i,u,h);i=E0(this.calculateLandmarksBoundingBox(A),1.5);let g=Rn(A);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&_r.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=F0.count?F0.symmetryLine:$8.symmetryLine;u=ug(i.landmarks[w],i.landmarks[b]);let k=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],E=Le.rotateWithOffset(t.toFloat(),u,0,N);h=M0(-u,k),c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let x={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:y},v=C0(i);return this.storedBoxes[o]={...v,landmarks:A,confidence:i.confidence,faceConfidence:f},x}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var mg=class{constructor(t,n,r,a){this.facePipeline=new pg(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Qr))l[h]=Qr[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxConfidence:Math.round(100*s.boxConfidence)/100,faceConfidence:Math.round(100*s.faceConfidence)/100,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},qt=[null,null,null];async function yg(e){return!qt[0]&&e.face.enabled||!qt[1]&&e.face.mesh.enabled||!qt[2]&&e.face.iris.enabled?(qt=await Promise.all([!qt[0]&&e.face.enabled?T8(e):null,!qt[1]&&e.face.mesh.enabled?ct(pt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!qt[2]&&e.face.iris.enabled?ct(pt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!qt[1]||!qt[1].modelUrl?le("load model failed:",e.face.mesh.modelPath):e.debug&&le("load model:",qt[1].modelUrl)),e.face.iris.enabled&&(!qt[2]||!qt[1].modelUrl?le("load model failed:",e.face.iris.modelPath):e.debug&&le("load model:",qt[2].modelUrl))):e.debug&&(le("cached model:",qt[0].model.modelUrl),le("cached model:",qt[1].modelUrl),le("cached model:",qt[2].modelUrl)),new mg(qt[0],qt[1],qt[2],e)}var D8=Bi,O8=eh;var Sg={};lr(Sg,{PoseNet:()=>Ng,load:()=>Tg});function Fie(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var Ag=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Fie(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function gg(e){return Math.floor(e/2)}var xg=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(gg(t),t);)this.exchange(t,gg(t)),t=gg(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function $ie(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function z8(e,t,n){let[r,a,s]=n.shape,i=new xg(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||$ie(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}var ru=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],au=ru.length,th=ru.reduce((e,t,n)=>(e[t]=n,e),{}),Die=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],lle=Die.map(([e,t])=>[th[e],th[t]]),P8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function wg(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+au)}}function D0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=wg(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function bg(e,t,n){return e<t?t:e>n?n:e}function L8(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function _g(e,t){return{x:e.x+t.x,y:e.y+t.y}}function W8(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function Oie(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+au)}}function zie(e,t){let n=[];for(let r=0;r<au;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=Oie(a,s,r,t);n.push(o),n.push(i)}return Rn(n,[au,2])}function B8(e,t,n){return z(()=>e.toTensor().mul(xe(t,"int32")).toFloat().add(zie(e,n)))}function Pie(e,t){return z(()=>{let n=e.div(xe(t,"int32"));return e.sub(n.mul(xe(t,"int32")))})}function V8(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(xe(n,"int32")).expandDims(1),o=Pie(s,n).expandDims(1);return rt([i,o],1)})}var j8=P8.map(([e,t])=>[th[e],th[t]]),vg=j8.map(([,e])=>e),U8=j8.map(([e])=>e),Lie=16;function Wie(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function kg(e,t,n,r){return{y:bg(Math.round(e.y/t),0,n-1),x:bg(Math.round(e.x/t),0,r-1)}}function H8(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=kg(t.position,s,l,c),h=Wie(e,u,i),p=_g(t.position,h);for(let y=0;y<o;y++){let A=kg(p,s,l,c),g=wg(A.y,A.x,n,a);p=_g({x:A.x*s,y:A.y*s},{x:g.x,y:g.y})}let f=kg(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:ru[n],score:m}}function G8(e,t,n,r,a,s){let i=t.shape[2],o=vg.length,l=new Array(i),{part:c,score:u}=e,h=D0(c,r,n);l[c.id]={score:u,part:ru[c.id],position:h};for(let d=o-1;d>=0;--d){let p=vg[d],f=U8[d];l[p]&&!l[f]&&(l[f]=H8(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=U8[d],f=vg[d];l[p]&&!l[f]&&(l[f]=H8(d,l[p],f,t,n,r,a))}return l}async function q8(e,t,n){let r=0,a=V8(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=B8(l,Lie,o),u=await c.buffer(),d=Array.from(W8(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:ru[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var Bie=1,X8=16;function K8(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return L8(r,n,i.y,i.x)<=t})}function Vie(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(K8(e,t,s,o)||(a+=i),a),0)/n.length}function Z8(e,t,n,r,a,s,i){let o=[],l=z8(i,Bie,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=D0(u.part,X8,t);if(K8(o,c,h,u.part.id))continue;let d=G8(u,e,t,X8,n,r),p=Vie(o,c,d);p>i&&o.push({keypoints:d,score:Math.round(100*p)/100})}return o}async function Y8(e){return Promise.all(e.map(t=>t.buffer()))}function 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n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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lk=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],uk=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var xn;async function Pg(e){return xn?e.debug&&le("cached model:",xn.modelUrl):(xn=await ct(pt(e.modelBasePath,e.body.modelPath)),xn.width=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[2].size),xn.height=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!xn||!xn.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",xn.modelUrl)),xn}async function Lg(e,t){if(!xn||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Le.resizeBilinear(e,[xn.width,xn.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let u=await an(()=>xn.predict(a));s=u.result.find(h=>h.size===195||h.size===155).dataSync(),u.result.forEach(h=>h.dispose()),gn("blazepose",u)}else{let u=await xn.predict(a);s=u.find(h=>h.size===195||h.size===155).dataSync(),u.forEach(h=>h.dispose())}a.dispose();let i=[],o=s.length===195?lk:uk,l=5;for(let u=0;u<s.length/l;u++)i.push({id:u,part:o[u],position:{x:Math.trunc(n.width*s[l*u+0]/255),y:Math.trunc(n.height*s[l*u+1]/255),z:Math.trunc(s[l*u+2])+0},score:(100-Math.trunc(100/(1+Math.exp(s[l*u+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(s[l*u+4]))))/100});return[{score:i.reduce((u,h)=>h.score>u?h.score:u,0),keypoints:i}]}var wn,rh=[],L0=Number.MAX_SAFE_INTEGER,Yie=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function Wg(e){return wn?e.debug&&le("cached model:",wn.modelUrl):(wn=await ct(pt(e.modelBasePath,e.body.modelPath)),!wn||!wn.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",wn.modelUrl)),wn}function Jie(e,t){let[n,r]=e.shape;return z(()=>{let a=(o,l)=>ye(o,P(me(o,xe(l,"int32")),xe(l,"int32"))),s=H(e,[r*n]),i=Cn(s,0).dataSync()[0];if(i>t){let 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K8({canvas:wt}):null),!Mt)return{tensor:null,canvas:Re};Mt.reset(),Mt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&Mt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&Mt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&Mt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&Mt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&Mt.addFilter("hue",t.filter.hue),t.filter.negative&&Mt.addFilter("negative"),t.filter.sepia&&Mt.addFilter("sepia"),t.filter.vintage&&Mt.addFilter("brownie"),t.filter.sepia&&Mt.addFilter("sepia"),t.filter.kodachrome&&Mt.addFilter("kodachrome"),t.filter.technicolor&&Mt.addFilter("technicolor"),t.filter.polaroid&&Mt.addFilter("polaroid"),t.filter.pixelate!==0&&Mt.addFilter("pixelate",t.filter.pixelate),Mt.apply(Re)}else wt=Re,Mt&&(Mt=null);let c;if(wt.data){let h=[wt.height,wt.width,3];c=md(wt.data,h,"int32")}else if(wt instanceof ImageData)c=ui.fromPixels(wt);else 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ht={backend:"webgl",modelBasePath:"../models/",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"},age:{enabled:!1,modelPath:"age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"mobileface.json"}},body:{enabled:!0,modelPath:"posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var Li={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function B0(e,t,n,r=0,a){e.fillStyle=a.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:a.color,e.beginPath(),e.arc(t,n,a.pointSize,0,2*Math.PI),e.fill()}function tu(e,t,n,r,a,s){if(e.beginPath(),s.useCurves){let i=(t+t+r)/2,o=(n+n+a)/2;e.ellipse(i,o,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+r-s.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+s.roundRect),e.lineTo(t+r,n+a-s.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-s.roundRect,n+a),e.lineTo(t+s.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function $g(e,t=[],n){if(!(t===void 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c=o[1]>0?`#${o[1]}`:"",u=`${o[0]} ${c}: ${l[1]}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(u,8,2+s*r.lineHeight)),a.fillStyle=r.labelColor,a.fillText(u,6,0+s*r.lineHeight),s+=1}}}async function Y8(e,t,n){let r=Yn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a)for(let s of t){a.font=r.font,a.strokeStyle=r.color,a.fillStyle=r.color,r.drawBoxes&&(r.useRawBoxes?tu(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):tu(a,s.box[0],s.box[1],s.box[2],s.box[3],r));let i=[];if(i.push(`face confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}s.rotation&&s.rotation.angle&&s.rotation.angle.roll&&i.push(`roll: ${Math.trunc(100*s.rotation.angle.roll)/100} yaw:${Math.trunc(100*s.rotation.angle.yaw)/100} pitch:${Math.trunc(100*s.rotation.angle.pitch)/100}`),i.length===0&&i.push("face"),a.fillStyle=r.color;for(let o=i.length-1;o>=0;o--){let l=Math.max(s.box[0],0),c=o*r.lineHeight+s.box[1];r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i[o],l+5,c+16)),a.fillStyle=r.labelColor,a.fillText(i[o],l+4,c+15)}if(a.lineWidth=1,s.mesh&&s.mesh.length>0){if(r.drawPoints)for(let o of s.mesh)B0(a,o[0],o[1],o[2],r);if(r.drawPolygons){a.lineWidth=1;for(let o=0;o<zi.length/3;o++){let l=[zi[o*3+0],zi[o*3+1],zi[o*3+2]].map(c=>s.mesh[c]);$g(a,l,r)}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;a.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;a.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}}}}}var rs=[];async function J8(e,t,n){let r=Yn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round";for(let s=0;s<t.length;s++){if(!rs[s]&&r.bufferedOutput&&(rs[s]={...t[s]}),a.strokeStyle=r.color,a.lineWidth=r.lineWidth,r.drawPoints)for(let i=0;i<t[s].keypoints.length;i++)a.fillStyle=r.useDepth&&t[s].keypoints[i].position.z?`rgba(${127.5+2*t[s].keypoints[i].position.z}, ${127.5-2*t[s].keypoints[i].position.z}, 255, 0.5)`:r.color,r.bufferedOutput?(rs[s].keypoints[i][0]=(rs[s].keypoints[i][0]+t[s].keypoints[i].position.x)/2,rs[s].keypoints[i][1]=(rs[s].keypoints[i][1]+t[s].keypoints[i].position.y)/2,B0(a,rs[s].keypoints[i][0],rs[s].keypoints[i][1],0,r)):B0(a,t[s].keypoints[i].position.x,t[s].keypoints[i].position.y,0,r);if(r.drawLabels&&(a.font=r.font,t[s].keypoints))for(let i of t[s].keypoints)a.fillStyle=r.useDepth&&i.position.z?`rgba(${127.5+2*i.position.z}, ${127.5-2*i.position.z}, 255, 0.5)`:r.color,a.fillText(`${i.part}`,i.position.x+4,i.position.y+4);if(r.drawPolygons&&t[s].keypoints){let i,o=[];o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),o.length===4&&$g(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftKnee"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftAnkle"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHeel"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftFoot"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightKnee"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightAnkle"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHeel"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightFoot"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftElbow"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftWrist"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftPalm"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightElbow"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightWrist"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightPalm"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r)}}}}async function Q8(e,t,n){let r=Yn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round",a.font=r.font;for(let s of t){if(r.drawBoxes&&(a.strokeStyle=r.color,a.fillStyle=r.color,r.useRawBoxes?tu(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):tu(a,s.box[0],s.box[1],s.box[2],s.box[3],r),r.drawLabels&&(r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText("hand",s.box[0]+3,1+s.box[1]+r.lineHeight,s.box[2])),a.fillStyle=r.labelColor,a.fillText("hand",s.box[0]+2,0+s.box[1]+r.lineHeight,s.box[2])),a.stroke()),r.drawPoints&&s.landmarks&&s.landmarks.length>0)for(let i of s.landmarks)a.fillStyle=r.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:r.color,B0(a,i[0],i[1],0,r);if(r.drawPolygons){let i=o=>{if(!!o)for(let l=0;l<o.length;l++)a.lineWidth=r.lineWidth,a.beginPath(),a.strokeStyle=r.useDepth?`rgba(${127.5+2*o[l][2]}, ${127.5-2*o[l][2]}, 255, 0.5)`:r.color,a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()};i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function ek(e,t,n){let r=Yn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round",a.font=r.font;for(let s of t)if(r.drawBoxes){if(a.strokeStyle=r.color,a.fillStyle=r.color,r.useRawBoxes?tu(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):tu(a,s.box[0],s.box[1],s.box[2],s.box[3],r),r.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+r.lineHeight,s.box[2])),a.fillStyle=r.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+r.lineHeight,s.box[2])}a.stroke()}}}async function xie(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function wie(e,t,n){let r=Yn(Li,n);!t||!e||e instanceof HTMLCanvasElement&&(Y8(e,t.face,r),J8(e,t.body,r),Q8(e,t.hand,r),Z8(e,t.gesture,r),ek(e,t.object,r))}var V0=`
+`),h.brightness=function(w){let b=(w||0)+1;h.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},h.saturation=function(w){let b=(w||0)*2/3+1,k=(b-1)*-.5;h.colorMatrix([b,k,k,0,0,k,b,k,0,0,k,k,b,0,0,0,0,0,1,0])},h.desaturate=function(){h.saturation(-1)},h.contrast=function(w){let b=(w||0)+1,k=-128*(b-1);h.colorMatrix([b,0,0,0,k,0,b,0,0,k,0,0,b,0,k,0,0,0,1,0])},h.negative=function(){h.contrast(-2)},h.hue=function(w){w=(w||0)/180*Math.PI;let b=Math.cos(w),k=Math.sin(w),N=.213,E=.715,F=.072;h.colorMatrix([N+b*(1-N)+k*-N,E+b*-E+k*-E,F+b*-F+k*(1-F),0,0,N+b*-N+k*.143,E+b*(1-E)+k*.14,F+b*-F+k*-.283,0,0,N+b*-N+k*-(1-N),E+b*-E+k*E,F+b*(1-F)+k*F,0,0,0,0,0,1,0])},h.desaturateLuminance=function(){h.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},h.sepia=function(){h.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},h.brownie=function(){h.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},h.vintagePinhole=function(){h.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},h.kodachrome=function(){h.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},h.technicolor=function(){h.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},h.polaroid=function(){h.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},h.shiftToBGR=function(){h.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},h.convolution=function(w){let b=new Float32Array(w),k=1/o,N=1/l,E=v(h.convolution.SHADER);m.uniform1fv(E.uniform.m,b),m.uniform2f(E.uniform.px,k,N),x()},h.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
+`),h.detectEdges=function(){h.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},h.sobelX=function(){h.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},h.sobelY=function(){h.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},h.sharpen=function(w){let b=w||1;h.convolution.call(this,[0,-1*b,0,-1*b,1+4*b,-1*b,0,-1*b,0])},h.emboss=function(w){let b=w||1;h.convolution.call(this,[-2*b,-1*b,0,-1*b,1,1*b,0,1*b,2*b])},h.blur=function(w){let b=w/7/o,k=w/7/l,N=v(h.blur.SHADER);m.uniform2f(N.uniform.px,0,k),x(f.INTERMEDIATE),m.uniform2f(N.uniform.px,b,0),x()},h.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;","}"].join(`
+`),h.pixelate=function(w){let b=w/o,k=w/l,N=v(h.pixelate.SHADER);m.uniform2f(N.uniform.size,b,k),x()},h.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
+`)}var j0=2048,Re,wt,$t;function qg(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof ze)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof ze)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Wr(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let a=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=a,o=s;if(i>j0&&(i=j0,o=i*s/a),o>j0&&(o=j0,i=o*a/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Re||(Re==null?void 0:Re.width)!==i||(Re==null?void 0:Re.height)!==o)&&(Re=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Re==null?void 0:Re.width)!==i&&(Re.width=i),(Re==null?void 0:Re.height)!==o&&(Re.height=o));let l=Re.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Re==null?void 0:Re.width,Re==null?void 0:Re.height),t.filter.enabled){if((!$t||!wt||Re.width!==wt.width||(Re==null?void 0:Re.height)!==(wt==null?void 0:wt.height))&&(wt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Re==null?void 0:Re.width,Re==null?void 0:Re.height):document.createElement("canvas"),(wt==null?void 0:wt.width)!==(Re==null?void 0:Re.width)&&(wt.width=Re==null?void 0:Re.width),(wt==null?void 0:wt.height)!==(Re==null?void 0:Re.height)&&(wt.height=Re==null?void 0:Re.height),$t=_r.flags.IS_BROWSER?new fk({canvas:wt}):null),!$t)return{tensor:null,canvas:Re};$t.reset(),$t.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&$t.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&$t.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&$t.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&$t.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&$t.addFilter("hue",t.filter.hue),t.filter.negative&&$t.addFilter("negative"),t.filter.sepia&&$t.addFilter("sepia"),t.filter.vintage&&$t.addFilter("brownie"),t.filter.sepia&&$t.addFilter("sepia"),t.filter.kodachrome&&$t.addFilter("kodachrome"),t.filter.technicolor&&$t.addFilter("technicolor"),t.filter.polaroid&&$t.addFilter("polaroid"),t.filter.pixelate!==0&&$t.addFilter("pixelate",t.filter.pixelate),$t.apply(Re)}else wt=Re,$t&&($t=null);let c;if(wt.data){let h=[wt.height,wt.width,3];c=xd(wt.data,h,"int32")}else if(wt instanceof ImageData)c=pi.fromPixels(wt);else if(t.backend==="webgl"||t.backend==="humangl"){let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(wt,0,0),c=pi.fromPixels(h)}else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(wt,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);c=pi.fromPixels(p)}let u=c.toFloat();n=u.expandDims(0),c.dispose(),u.dispose()}let r=t.filter.return?wt:null;return{tensor:n,canvas:r}}var Xg={};lr(Xg,{all:()=>noe,body:()=>Ak,canvas:()=>toe,face:()=>yk,gesture:()=>mk,hand:()=>gk,object:()=>xk,options:()=>ji});var ht={backend:"webgl",modelBasePath:"../models/",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"},age:{enabled:!1,modelPath:"age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"mobileface.json"}},body:{enabled:!0,modelPath:"posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var ji={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function U0(e,t,n,r=0,a){e.fillStyle=a.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:a.color,e.beginPath(),e.arc(t,n,a.pointSize,0,2*Math.PI),e.fill()}function su(e,t,n,r,a,s){if(e.beginPath(),s.useCurves){let i=(t+t+r)/2,o=(n+n+a)/2;e.ellipse(i,o,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+r-s.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+s.roundRect),e.lineTo(t+r,n+a-s.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-s.roundRect,n+a),e.lineTo(t+s.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function Kg(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let r of t)e.strokeStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.lineTo(r[0],parseInt(r[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function ah(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){Kg(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let r=0;r<t.length-2;r++){let a=(t[r][0]+t[r+1][0])/2,s=(t[r][1]+t[r+1][1])/2;e.quadraticCurveTo(t[r][0],t[r][1],a,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function mk(e,t,n){let r=Jn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!a)return;a.font=r.font,a.fillStyle=r.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let c=o[1]>0?`#${o[1]}`:"",u=`${o[0]} ${c}: ${l[1]}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(u,8,2+s*r.lineHeight)),a.fillStyle=r.labelColor,a.fillText(u,6,0+s*r.lineHeight),s+=1}}}async function yk(e,t,n){let r=Jn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a)for(let s of t){a.font=r.font,a.strokeStyle=r.color,a.fillStyle=r.color,r.drawBoxes&&(r.useRawBoxes?su(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):su(a,s.box[0],s.box[1],s.box[2],s.box[3],r));let i=[];if(i.push(`face confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}s.rotation&&s.rotation.angle&&s.rotation.angle.roll&&i.push(`roll: ${Math.trunc(100*s.rotation.angle.roll)/100} yaw:${Math.trunc(100*s.rotation.angle.yaw)/100} pitch:${Math.trunc(100*s.rotation.angle.pitch)/100}`),i.length===0&&i.push("face"),a.fillStyle=r.color;for(let o=i.length-1;o>=0;o--){let l=Math.max(s.box[0],0),c=o*r.lineHeight+s.box[1];r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i[o],l+5,c+16)),a.fillStyle=r.labelColor,a.fillText(i[o],l+4,c+15)}if(a.lineWidth=1,s.mesh&&s.mesh.length>0){if(r.drawPoints)for(let o of s.mesh)U0(a,o[0],o[1],o[2],r);if(r.drawPolygons){a.lineWidth=1;for(let o=0;o<Bi.length/3;o++){let l=[Bi[o*3+0],Bi[o*3+1],Bi[o*3+2]].map(c=>s.mesh[c]);Kg(a,l,r)}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;a.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;a.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}}}}}var ss=[];async function Ak(e,t,n){let r=Jn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round";for(let s=0;s<t.length;s++){if(!ss[s]&&r.bufferedOutput&&(ss[s]={...t[s]}),a.strokeStyle=r.color,a.lineWidth=r.lineWidth,r.drawPoints)for(let i=0;i<t[s].keypoints.length;i++)a.fillStyle=r.useDepth&&t[s].keypoints[i].position.z?`rgba(${127.5+2*t[s].keypoints[i].position.z}, ${127.5-2*t[s].keypoints[i].position.z}, 255, 0.5)`:r.color,r.bufferedOutput?(ss[s].keypoints[i][0]=(ss[s].keypoints[i][0]+t[s].keypoints[i].position.x)/2,ss[s].keypoints[i][1]=(ss[s].keypoints[i][1]+t[s].keypoints[i].position.y)/2,U0(a,ss[s].keypoints[i][0],ss[s].keypoints[i][1],0,r)):U0(a,t[s].keypoints[i].position.x,t[s].keypoints[i].position.y,0,r);if(r.drawLabels&&(a.font=r.font,t[s].keypoints))for(let i of t[s].keypoints)a.fillStyle=r.useDepth&&i.position.z?`rgba(${127.5+2*i.position.z}, ${127.5-2*i.position.z}, 255, 0.5)`:r.color,a.fillText(`${i.part}`,i.position.x+4,i.position.y+4);if(r.drawPolygons&&t[s].keypoints){let 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-2Q==`;var tk="1.5.1";var nu,Qc,eh,Wi,U0,th,H0,G0,q0,_ie=class{constructor(t={}){nu.set(this,void 0);Qc.set(this,void 0);eh.set(this,void 0);Wi.set(this,void 0);this.analyze=(...t)=>{if(!or(this,Qc))return;let n=this.tf.engine().state.numTensors,r=or(this,nu);os(this,nu,n);let a=n-r;a!==0&&le(...t,a)};U0.set(this,t=>{if(!or(this,eh))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof We))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});th.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let r=Ye();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&le("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&le("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&le(`wasm execution: ${a?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),a||le("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&a8();try{await this.tf.setBackend(this.config.backend)}catch(a){le("error: cannot set backend:",this.config.backend,a)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(le("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let a=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&le(`gl version:${a.getParameter(a.VERSION)} renderer:${a.getParameter(a.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-r)}});H0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(V0);break;case"full":n=await t(j0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});G0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+V0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+j0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));q0.set(this,async()=>{var i;if(typeof void 0=="undefined")return this.config.debug&&le("Warmup tfjs-node not loaded"),null;let t=o=>Buffer.from(o,"base64"),n;if(this.config.warmup==="face"&&(n=t(V0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(j0)),!n)return null;let r=(i=void 0)==null?void 0:i.decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=du,this.draw=Dg,this.version=tk,this.config=Yn(ht,t),this.state="idle",os(this,nu,0),os(this,Qc,!1),os(this,eh,!1),os(this,Wi,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>Fg(n,this.config),this.classes={facemesh:eg,age:R2,gender:F2,emotion:P2,faceres:U2,body:this.config.body.modelPath.includes("posenet")?dg:vg,hand:xg,nanodet:Tg},this.faceTriangulation=A8,this.faceUVMap=y8,this.sysinfo=Kg()}profileData(){return this.config.profile?m0:{}}similarity(t,n){return this.config.face.description.enabled?G2(t,n):this.config.face.embedding.enabled?s8(t,n):0}enhance(t){return q2(t)}match(t,n,r=0){return i8(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=Yn(this.config,t)),or(this,Wi)&&(this.config.debug&&le(`version: ${this.version}`),this.config.debug&&le(`tfjs version: ${this.tf.version_core}`),this.config.debug&&le("platform:",this.sysinfo.platform),this.config.debug&&le("agent:",this.sysinfo.agent),await or(this,th).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&le("configuration:",this.config),this.config.debug&&le("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?ng(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?M2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?z2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?B2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?V2(this.config):null),this.models.handpose||(this.config.hand.enabled?_g(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?fg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?kg(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?Ng(this.config):null),this.models.nanodet||(this.config.object.enabled?Cg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?H2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await ng(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await M2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await z2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await B2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await V2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await _g(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await fg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await kg(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Ng(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await Cg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await H2(this.config))),or(this,Wi)&&(this.config.debug&&le("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),os(this,Wi,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var A,y,g,w;this.state="config";let a;this.config=Yn(this.config,n),this.state="check";let s=or(this,U0).call(this,t);s&&(le(s,t),r({error:s}));let i=Ye();await or(this,th).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&Mg instanceof ImageBitmap||t instanceof We)&&(le("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),a=Ye();let l=Fg(t,this.config);if(!l||!l.tensor){le("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let c,u,h,d,p;this.config.async?(h=this.config.face.enabled?X2(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),h=this.config.face.enabled?await X2(this,l.tensor):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.face=p)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?(A=this.models.posenet)==null?void 0:A.estimatePoses(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?Ig(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?Sg(l.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await((y=this.models.posenet)==null?void 0:y.estimatePoses(l.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await Ig(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?await Sg(l.tensor,this.config):[]),p=Math.trunc(Ye()-a),p>0&&(this.perf.body=p)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(g=this.models.handpose)==null?void 0:g.estimateHands(l.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),u=this.config.hand.enabled?await((w=this.models.handpose)==null?void 0:w.estimateHands(l.tensor,this.config)):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.hand=p)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(d=this.config.object.enabled?Rg(l.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),d=this.config.object.enabled?await Rg(l.tensor,this.config):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.object=p)),this.analyze("End Object:"),this.config.async&&([h,c,u,d]=await Promise.all([h,c,u,d])),_e(l.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(a=Ye(),f=[...G8(h),...H8(c),...X8(u),...q8(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let m={face:h,body:c,hand:u,gesture:f,object:d,performance:this.perf,canvas:l.canvas};r(m)})}async warmup(t={}){let n=Ye();if(t&&(this.config=Yn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await or(this,H0).call(this):typeof Image!="undefined"?a=await or(this,G0).call(this):a=await or(this,q0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&le("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};nu=new WeakMap,Qc=new WeakMap,eh=new WeakMap,Wi=new WeakMap,U0=new WeakMap,th=new WeakMap,H0=new WeakMap,G0=new WeakMap,q0=new WeakMap;export{_ie as Human,_ie as default};
+2Q==`;var wk="1.5.1";var iu,sh,ih,Ui,q0,oh,X0,K0,Z0,aoe=class{constructor(t={}){iu.set(this,void 0);sh.set(this,void 0);ih.set(this,void 0);Ui.set(this,void 0);this.analyze=(...t)=>{if(!ur(this,sh))return;let n=this.tf.engine().state.numTensors,r=ur(this,iu);us(this,iu,n);let a=n-r;a!==0&&le(...t,a)};q0.set(this,t=>{if(!ur(this,ih))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof ze))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});oh.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let r=Ye();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&le("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&le("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&le(`wasm execution: ${a?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!a&&le("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&v8();try{await this.tf.setBackend(this.config.backend)}catch(a){le("error: cannot set backend:",this.config.backend,a)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(le("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let a=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&le(`gl version:${a.getParameter(a.VERSION)} renderer:${a.getParameter(a.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-r)}});X0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(H0);break;case"full":n=await t(G0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});K0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+H0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+G0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));Z0.set(this,async()=>{let t=a=>Buffer.from(a,"base64"),n;if(this.config.warmup==="face"&&(n=t(H0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(G0)),!n)return null;let r;if(typeof void 0!="undefined"){let a=(void 0).decodeJpeg(n),s=a.expandDims(0);this.tf.dispose(a),r=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&le("Warmup tfjs-node not loaded");return r});this.tf=gu,this.draw=Xg,this.version=wk,this.config=Jn(ht,t),this.state="idle",us(this,iu,0),us(this,sh,!1),us(this,ih,!1),us(this,Ui,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>qg(n,this.config),this.classes={facemesh:fg,age:H2,gender:q2,emotion:J2,faceres:ag,body:this.config.body.modelPath.includes("posenet")?Sg:zg,hand:Fg,nanodet:Vg},this.faceTriangulation=D8,this.faceUVMap=O8,this.sysinfo=u5()}profileData(){return this.config.profile?g0:{}}similarity(t,n){return this.config.face.description.enabled?ig(t,n):this.config.face.embedding.enabled?k8(t,n):0}enhance(t){return og(t)}match(t,n,r=0){return I8(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=Jn(this.config,t)),ur(this,Ui)&&(this.config.debug&&le(`version: ${this.version}`),this.config.debug&&le(`tfjs version: ${this.tf.version_core}`),this.config.debug&&le("platform:",this.sysinfo.platform),this.config.debug&&le("agent:",this.sysinfo.agent),await ur(this,oh).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&le("configuration:",this.config),this.config.debug&&le("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?yg(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?G2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Y2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?tg(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?ng(this.config):null),this.models.handpose||(this.config.hand.enabled?Og(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?Tg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?Pg(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?Wg(this.config):null),this.models.nanodet||(this.config.object.enabled?Ug(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?sg(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await yg(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await G2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Y2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await tg(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await ng(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await Og(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await Tg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await Pg(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Wg(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await Ug(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await sg(this.config))),ur(this,Ui)&&(this.config.debug&&le("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),us(this,Ui,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var y,A,g,x;this.state="config";let a;this.config=Jn(this.config,n),this.state="check";let s=ur(this,q0).call(this,t);s&&(le(s,t),r({error:s}));let i=Ye();await ur(this,oh).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&Gg instanceof ImageBitmap||t instanceof ze)&&(le("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),a=Ye();let l=qg(t,this.config);if(!l||!l.tensor){le("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let c,u,h,d,p;this.config.async?(h=this.config.face.enabled?lg(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),h=this.config.face.enabled?await lg(this,l.tensor):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.face=p)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?(y=this.models.posenet)==null?void 0:y.estimatePoses(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?Lg(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?Bg(l.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await((A=this.models.posenet)==null?void 0:A.estimatePoses(l.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await Lg(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?await Bg(l.tensor,this.config):[]),p=Math.trunc(Ye()-a),p>0&&(this.perf.body=p)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(g=this.models.handpose)==null?void 0:g.estimateHands(l.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),u=this.config.hand.enabled?await((x=this.models.handpose)==null?void 0:x.estimateHands(l.tensor,this.config)):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.hand=p)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(d=this.config.object.enabled?Hg(l.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),d=this.config.object.enabled?await Hg(l.tensor,this.config):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.object=p)),this.analyze("End Object:"),this.config.async&&([h,c,u,d]=await Promise.all([h,c,u,d])),_e(l.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(a=Ye(),f=[...hk(h),...ck(c),...pk(u),...dk(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let m={face:h,body:c,hand:u,gesture:f,object:d,performance:this.perf,canvas:l.canvas};r(m)})}async warmup(t={}){let n=Ye();if(t&&(this.config=Jn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ur(this,X0).call(this):typeof Image!="undefined"?a=await ur(this,K0).call(this):a=await ur(this,Z0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&le("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};iu=new WeakMap,sh=new WeakMap,ih=new WeakMap,Ui=new WeakMap,q0=new WeakMap,oh=new WeakMap,X0=new WeakMap,K0=new WeakMap,Z0=new WeakMap;export{aoe as Human,aoe as default};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
index 109891e9..30963b48 100644
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
@@ -1,7 +1,7 @@
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-  "sourcesContent": ["// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModuleThreadedSimd) {\n  WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var 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_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=function(){return(_emscripten_register_main_browser_thread_id=Module[\"_emscripten_register_main_browser_thread_id\"]=Module[\"asm\"][\"gb\"]).apply(null,arguments)};var __emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=function(){return(__emscripten_do_dispatch_to_thread=Module[\"__emscripten_do_dispatch_to_thread\"]=Module[\"asm\"][\"hb\"]).apply(null,arguments)};var _emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=function(){return(_emscripten_sync_run_in_main_thread_4=Module[\"_emscripten_sync_run_in_main_thread_4\"]=Module[\"asm\"][\"ib\"]).apply(null,arguments)};var _emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=function(){return(_emscripten_run_in_main_runtime_thread_js=Module[\"_emscripten_run_in_main_runtime_thread_js\"]=Module[\"asm\"][\"jb\"]).apply(null,arguments)};var __emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=function(){return(__emscripten_call_on_thread=Module[\"__emscripten_call_on_thread\"]=Module[\"asm\"][\"kb\"]).apply(null,arguments)};var _emscripten_tls_init=Module[\"_emscripten_tls_init\"]=function(){return(_emscripten_tls_init=Module[\"_emscripten_tls_init\"]=Module[\"asm\"][\"lb\"]).apply(null,arguments)};var __emscripten_thread_init=Module[\"__emscripten_thread_init\"]=function(){return(__emscripten_thread_init=Module[\"__emscripten_thread_init\"]=Module[\"asm\"][\"mb\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"nb\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"ob\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"pb\"]).apply(null,arguments)};var _emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=function(){return(_emscripten_stack_set_limits=Module[\"_emscripten_stack_set_limits\"]=Module[\"asm\"][\"qb\"]).apply(null,arguments)};var _memalign=Module[\"_memalign\"]=function(){return(_memalign=Module[\"_memalign\"]=Module[\"asm\"][\"rb\"]).apply(null,arguments)};var __emscripten_allow_main_runtime_queued_calls=Module[\"__emscripten_allow_main_runtime_queued_calls\"]=9880;var __emscripten_main_thread_futex=Module[\"__emscripten_main_thread_futex\"]=11368;Module[\"cwrap\"]=cwrap;Module[\"PThread\"]=PThread;Module[\"PThread\"]=PThread;Module[\"wasmMemory\"]=wasmMemory;Module[\"ExitStatus\"]=ExitStatus;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}if(ENVIRONMENT_IS_PTHREAD){readyPromiseResolve(Module);postMessage({\"cmd\":\"loaded\"});return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(!implicit){if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exitProcess\",\"returnCode\":status});throw new ExitStatus(status)}else{}}if(noExitRuntime){}else{PThread.terminateAllThreads();EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}if(ENVIRONMENT_IS_PTHREAD){noExitRuntime=false;PThread.initWorker()}run();\n\n\n  return WasmBackendModuleThreadedSimd.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModuleThreadedSimd;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModuleThreadedSimd; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var 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XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return 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Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1250, moduleBytes: 4013323, imports: 7, importBytes: 2255, outputBytes: 2991826, outputFiles: 'dist/tfjs.esm.js' }\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1253, moduleBytes: 4029357, imports: 7, importBytes: 2285, outputBytes: 2998298, outputFiles: 'dist/tfjs.esm.js' }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - compiled\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - sources\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export versions\nexport const version = {\n  tfjs: packageBundle?.version || undefined,\n  'tfjs-core': packageCore?.version || undefined,\n  'tfjs-data': packageData?.version || undefined,\n  'tfjs-layers': packageLayers?.version || undefined,\n  'tfjs-converter': packageConverter?.version || undefined,\n  'tfjs-backend-cpu': version_cpu || undefined,\n  'tfjs-backend-webgl': version_webgl || undefined,\n  'tfjs-backend-wasm': version_wasm || undefined,\n};\n// export const version = {};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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{\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n 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{\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore // typescript cannot infer type\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore // typescript cannot infer type\n        this.models.handpose,\n        // @ts-ignore // typescript cannot infer type\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    // @ts-ignore\n    if (typeof tf.node === 'undefined') {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      return null;\n    }\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    // @ts-ignore // tf.node is only defined when compiling for nodejs\n    const data = tf.node?.decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"],
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"../src/handpose/handpipeline.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/image.ts", "../src/image/imagefx.js", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts", "../src/human.ts"],
+  "sourcesContent": ["// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') 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nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var 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if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return 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initRuntime(){runtimeInitialized=true;if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"D\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){removeRunDependency(\"wasm-instantiate\")}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={9800:function(){throw\"Canceled!\"},9818:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";var pthread=PThread.pthreads[pthread_ptr];if(pthread){GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},runExitHandlersAndDeinitThread:function(tb,exitCode){Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0)},threadExit:function(exitCode){var tb=_pthread_self();if(tb){PThread.runExitHandlersAndDeinitThread(tb,exitCode);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlersAndDeinitThread(_pthread_self(),-1);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"v\":__emscripten_notify_thread_queue,\"b\":_abort,\"w\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"x\":_emscripten_num_logical_cores,\"s\":_emscripten_receive_on_main_thread_js,\"r\":_emscripten_resize_heap,\"t\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"u\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"q\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"z\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _All=Module[\"_All\"]=function(){return(_All=Module[\"_All\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Any=Module[\"_Any\"]=function(){return(_Any=Module[\"_Any\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var 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if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof 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8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write,\"g\":_pthread_create,\"h\":_sysconf};var asm=createWasm();var 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_RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var 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_Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ma\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Na\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Oa\"]).apply(null,arguments)};var ___errno_location=Module[\"___errno_location\"]=function(){return(___errno_location=Module[\"___errno_location\"]=Module[\"asm\"][\"Pa\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Qa\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ra\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"Sa\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModule(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1250, moduleBytes: 4013323, imports: 7, importBytes: 2255, outputBytes: 2991826, outputFiles: 'dist/tfjs.esm.js' }\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1253, moduleBytes: 4029357, imports: 7, importBytes: 2285, outputBytes: 2998298, outputFiles: 'dist/tfjs.esm.js' }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - compiled\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - sources\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export versions\nexport const version = {\n  tfjs: packageBundle?.version || undefined,\n  'tfjs-core': packageCore?.version || undefined,\n  'tfjs-data': packageData?.version || undefined,\n  'tfjs-layers': packageLayers?.version || undefined,\n  'tfjs-converter': packageConverter?.version || undefined,\n  'tfjs-backend-cpu': version_cpu || undefined,\n  'tfjs-backend-webgl': version_webgl || undefined,\n  'tfjs-backend-wasm': version_wasm || undefined,\n};\n// export const version = {};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input.toFloat(), angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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{\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.8125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.0625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n 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{\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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"import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        this.models.handpose,\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? <any>posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    let res;\n    if (typeof tf['node'] !== 'undefined') {\n      const data = tf['node'].decodeJpeg(img);\n      const expanded = data.expandDims(0);\n      this.tf.dispose(data);\n      // log('Input:', expanded);\n      res = await this.detect(expanded, this.config);\n      this.tf.dispose(expanded);\n    } else {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      /*\n      const input = await canvasJS.loadImage(img);\n      const canvas = canvasJS.createCanvas(input.width, input.height);\n      const ctx = canvas.getContext('2d');\n      ctx.drawImage(img, 0, 0, input.width, input.height);\n      res = await this.detect(input, this.config);\n      */\n    }\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n"],
+  "mappings": 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   "names": []
 }
diff --git a/dist/human.js b/dist/human.js
index c99887b4..33d3e454 100644
--- a/dist/human.js
+++ b/dist/human.js
@@ -5,31 +5,31 @@
   author: <https://github.com/vladmandic>'
   */
 
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r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Cf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Cf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let A=ld(p,this.backendName);M(A!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,y,g);let w=g.map(b=>{if(b.rank!=null)return b;let{dataId:_,shape:x,dtype:N}=b;return this.makeTensorFromDataId(_,x,N)});if(r){let b=this.getTensorsForGradient(p,f,w);n=this.saveTensorsForBackwardMode(b)}return w}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(A=>this.keep(this.clone(A))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,A),A}}let{inputs:c,attrs:u}=e,h=Cf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=_f(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*mf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Wu||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*mf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of 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+`;return d[d.length-1]=" "+d[d.length-1]+"]"+(s?"":f),d}function Uu(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Dt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Et(e),n!=null){let r=n.length;M(r===this.size,()=>`Length of values '${r}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||Hx(t,this.size),this.strides=ro(e)}set(e,...t){t.length===0&&(t=[0]),M(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let r of e){if(r<0||r>=this.shape[t]){let a=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(a)}t++}let n=e[e.length-1];for(let r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Pr().makeTensor(this.values,this.shape,this.dtype)}},Pr=null,ll=null,vI=null;function kI(e){Pr=e}function II(e){ll=e}function SI(e){vI=e}var ze=class{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Et(e),this.strides=ro(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return ll.buffer(this.shape,this.dtype,e)}bufferSync(){return ll.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ao(this.shape,e,this.dtype==="complex64")}arraySync(){return ao(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Pr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>fd(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Pr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>fd(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Pr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Pr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ll.print(this,e)}clone(){return this.throwIfDisposed(),ll.clone(this)}toString(e=!1){let t=this.dataSync();return _I(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ll.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Pr().makeVariable(this,e,t,n)}};Object.defineProperty(ze,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function Z(){return Rf("Tensor",()=>ze)}Z();var Hu=class extends ze{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!oa(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Pr().disposeTensor(this),this.dataId=e.dataId,Pr().incRef(this,null)}dispose(){Pr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Hu,Symbol.hasInstance,{value:e=>e instanceof ze&&e.assign!=null&&e.assign instanceof Function});var vr={};Me(vr,{assertTypesMatch:()=>iw,getTensorsInContainer:()=>Df,isTensorInList:()=>NI,makeTypesMatch:()=>vt});var Of;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(Of||(Of={}));var zf;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})(zf||(zf={}));var Pf;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(Pf||(Pf={}));var Lf;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(Lf||(Lf={}));var Wf;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(Wf||(Wf={}));var TI={float32:Lf,int32:zf,bool:Pf,complex64:Wf};function dr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return TI[e][t]}function yd(e){return dr(e,"int32")}function vt(e,t){if(e.dtype===t.dtype)return[e,t];let n=dr(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function iw(e,t){M(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function NI(e,t){return t.some(n=>n.id===e.id)}function Df(e){let t=[],n=new Set;return ow(e,t,n),t}function ow(e,t,n){if(e==null)return;if(e instanceof ze){t.push(e);return}if(!EI(e))return;let r=e;for(let a in r){let s=r[a];n.has(s)||(n.add(s),ow(s,t,n))}}function EI(e){return Array.isArray(e)||typeof e=="object"}function Bf(e){return e.kernelName!=null}var lw=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Gu=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new lw}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(console.warn(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new gI(this.backendInstance),!0}setupRegisteredKernels(){ol(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){ol(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof wu)&&typeof n.then=="function"){let r=++this.pendingBackendInitId,a=n.then(s=>r<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(r<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let r;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(r),()=>(r=t(),r instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),r))}scopedRun(e,t,n){e();try{let r=n();return t(),r}catch(r){throw t(),r}}nextTensorId(){return Gu.nextTensorId++}nextVariableId(){return Gu.nextVariableId++}clone(e){let t=$.runKernel(Es,{x:e}),n={x:e},r=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return $.runKernel(ys,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,n,[t],r,a,{}),t}runKernel(e,t,n){if(dd(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let r=this.backend.numDataIds(),a=0;n.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=r-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Bf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Bf(e)){let{kernelName:p,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let y=dd(p,this.backendName);M(y!=null,()=>`Cannot find registered kernel '${p}' for backend '${this.backendName}'`),i=()=>{let A=this.backend.numDataIds();o=y.kernelFunc({inputs:f,attrs:m,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(p,A,g);let x=g.map(v=>{if(v.rank!=null)return v;let{dataId:w,shape:b,dtype:k}=v;return this.makeTensorFromDataId(w,b,k)});if(r){let v=this.getTensorsForGradient(p,f,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:p}=e,f=m=>{!r||(n=m.map(y=>this.keep(this.clone(y))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>p(this.backend,f));let y=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,y),y}}let{inputs:c,attrs:u}=e,h=Bf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(l,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),r&&this.addTapeNode(l,c,t,h,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(p=>c[p]!=null?c[p].shape:null),outputShapes:t.map(p=>p.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let r=Ff(e);if(r!=null){let a=r.inputsToSave||[],s=r.outputsToSave||[],i;r.saveAllInputs?(M(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=n.filter((l,c)=>s[c]);return i.concat(o)}return[]}makeTensor(e,t,n,r){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;n==="string"&&Ta(e[0])&&(a=e.map(o=>Bu(o)));let s=r.write(a,t,n),i=new ze(t,n,s,this.nextTensorId());if(this.trackTensor(i,r),n==="string"){let o=this.state.tensorInfo.get(s),l=Xx(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,n,r){n=n||"float32";let a=new ze(t,n,e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),r!=null&&r!==e.dtype&&(e=e.cast(r));let a=new Hu(e,t,n,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Sf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Hu||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Sf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(r=>r.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let r of 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r=R(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);M(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),M(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),M(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return $.runKernel(Iu,s,i)}var Qu=D({batchToSpaceND_:VN});function jN(e){let t;return e.rank===0||e.rank===1?t=H(e,[1,1,1,e.size]):e.rank===2?t=H(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=H(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function UN(e,t,n,r,a,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;r!=null&&(u=R(r,"offset","batchNorm")),M(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal 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${c.rank}.`),u!=null&&M(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var Jw=D({batchNorm2d_:HN});function GN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),M(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),M(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var Qw=D({batchNorm3d_:GN});function qN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),M(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),M(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),Ai(i,o,l,u,c,s)}var eb=D({batchNorm4d_:qN});function XN(e,t,n){let r=R(e,"x","bincount"),a=R(t,"weights","bincount");M(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),M(n>=0,()=>`size must be non-negative, but got 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xm=D({ceil_:ZN});function YN(e,t,n){let r=R(e,"x","clipByValue");M(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return $.runKernel(Ra,a,s)}var Tn=D({clipByValue_:YN});function JN(e){return rt(e,0)}var nb=D({concat1d_:JN});function QN(e,t){return rt(e,t)}var gl=D({concat2d_:QN});function eT(e,t){return rt(e,t)}var rb=D({concat3d_:eT});function tT(e,t){return rt(e,t)}var ab=D({concat4d_:tT});function nT(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","conv2d"),l=R(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=H(o,[1,o.shape[0],o.shape[1],o.shape[2]])),M(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&M(Vt(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];M(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),M(Br(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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Got stride ${n} and dilation '${s}'`),M(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=ca(d,h,[1,n],r,"NHWC",[1,s],i);return u?H(p,[p.shape[2],p.shape[3]]):H(p,[p.shape[0],p.shape[2],p.shape[3]])}var Id=D({conv1d_:rT});function aT(e,t,n,r,a,s="NHWC",i){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),M(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];M(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),M(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&M(Vt(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=$.runKernel(xs,d,p);return c?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var wm=D({conv2DBackpropInput_:aT});function sT(e,t,n,r,a,s){let i=R(e,"x","conv2dTranspose"),o=R(t,"filter","conv2dTranspose");return wm(n,i,o,r,a,"NHWC",s)}var Sd=D({conv2dTranspose_:sT});function iT(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=R(e,"x","conv3d"),o=R(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=H(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),M(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),M(Br(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. 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KC({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:i,bias:o,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:u}){if(l=l||"linear",Kd(D.state.gradientDepth,l)===!1){let _=ua(e,t,n,r,a,s,i);return o!=null&&(_=se(_,o)),Xd(_,l,c,u)}let h=C(e,"x","conv2d"),d=C(t,"filter","conv2d"),p=h,f=!1;h.rank===3&&(f=!0,p=H(h,[1,h.shape[0],h.shape[1],h.shape[2]])),M(p.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${p.rank}.`),M(d.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`),i!=null&&M(jt(r),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),M(p.shape[3]===d.shape[2],()=>`Error in conv2d: depth of input (${p.shape[3]}) must match input depth for filter ${d.shape[2]}.`),M(Wr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. 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a=C(e,"labels","hingeLoss"),s=C(t,"predictions","hingeLoss"),i=null;n!=null&&(i=C(n,"weights","hingeLoss")),rn(a.shape,s.shape,"Error in hingeLoss: ");let o=ge(1);a=Ae(P(ge(2),a),o);let l=Ur(Ae(o,P(a,s)));return da(l,i,r)}var GR=O({hingeLoss_:HR});function qR(e,t,n,r=1,a=hn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"labels","huberLoss"),i=C(t,"predictions","huberLoss"),o=null;n!=null&&(o=C(n,"weights","huberLoss")),rn(s.shape,i.shape,"Error in huberLoss: ");let l=ge(r),c=Ot(Ae(i,s)),u=xl(c,l),h=Ae(c,u),d=se(P(ge(.5),it(u)),P(l,h));return da(d,o,a)}var XR=O({huberLoss_:qR});function KR(e,t,n,r=1e-7,a=hn.SUM_BY_NONZERO_WEIGHTS){let s=C(e,"labels","logLoss"),i=C(t,"predictions","logLoss"),o=null;n!=null&&(o=C(n,"weights","logLoss")),rn(s.shape,i.shape,"Error in logLoss: ");let l=ge(1),c=ge(r),u=kt(P(s,On(se(i,c)))),h=P(Ae(l,s),On(se(Ae(l,i),c))),d=Ae(u,h);return da(d,o,a)}var ZR=O({logLoss_:KR});function YR(e,t,n,r=hn.SUM_BY_NONZERO_WEIGHTS){let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Qd.className="Adamax";Oa(Qd);var lc=class extends pa{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=D.registeredVariables[t];z(()=>{let s=se(P(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ut(ge(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
-============================`));let r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let r=this.readSync(n.real.dataId),a=this.readSync(n.imag.dataId);return R.mergeRealAndImagArrays(r,a)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}makeOutput(e,t,n){let r=this.write(e,t,n);return Lr().makeTensorFromDataId(r,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. 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n.makeTensorInfo(a.shape,a.dtype,m)}var xD={kernelName:Is,backendName:"cpu",kernelFunc:gD};function wD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=yt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=fr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=yt({inputs:{x:f},backend:n,attrs:{shape:u}}),A=bi({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),A}var bD={kernelName:xu,backendName:"cpu",kernelFunc:wD};function _D(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=Vm(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var 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a=r.map(o=>n.data.get(o.dataId).values),s=Be(r[0].shape,r[0].dtype),i=s.values;for(let o=0;o<r.length;o++){let l=a[o];for(let c=0;c<i.length;c++)i[c]+=l[c]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var c$={kernelName:ds,backendName:"cpu",kernelFunc:u$};function h$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"all");let o=_.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=nr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("all",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),f=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let g=A*p,x=m[g];for(let v=0;v<p;++v){let w=m[g+v];x=x&&w}f[A]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let y=n.makeTensorInfo(h,u.dtype,f);if(i){let A=C.expandShapeToKeepDim(h,o),g=mt({inputs:{x:y},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(y),g}return y}var d$={kernelName:lo,backendName:"cpu",kernelFunc:h$};function p$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"any");let o=_.parseAxisParam(s,a.shape),l=o,c=C.getAxesPermutation(l,a.shape.length),u=a;c!=null&&(u=nr({inputs:{x:a},backend:n,attrs:{perm:c}}),l=C.getInnerMostAxes(l.length,a.shape.length)),C.assertAxesAreInnerMostDims("any",l,u.shape.length);let[h,d]=C.computeOutAndReduceShapes(u.shape,l),p=_.sizeFromShape(d),f=_.makeZerosTypedArray(_.sizeFromShape(h),u.dtype),m=n.data.get(u.dataId).values;for(let A=0;A<f.length;++A){let g=A*p,x=m[g];for(let v=0;v<p;++v){let w=m[g+v];x=x||w}f[A]=x}c!=null&&n.disposeIntermediateTensorInfo(u);let y=n.makeTensorInfo(h,u.dtype,f);if(i){let A=C.expandShapeToKeepDim(h,o),g=mt({inputs:{x:y},backend:n,attrs:{shape:A}});return n.disposeIntermediateTensorInfo(y),g}return y}var f$={kernelName:uo,backendName:"cpu",kernelFunc:p$};function m$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;ve(a,"argMax");let i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=nr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMax",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),d=_.sizeFromShape(u),p=_.makeZerosTypedArray(d,"int32"),f=_.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let y=0;y<p.length;++y){let A=y*f,g=m[A],x=0;for(let v=0;v<f;++v){let w=m[A+v];w>g&&(g=w,x=v)}p[y]=x}return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(u,"int32",p)}var y$={kernelName:ps,backendName:"cpu",kernelFunc:m$};function A$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;ve(a,"argMin");let i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=nr({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),i=[i[0]],C.assertAxesAreInnerMostDims("argMin",i,l.shape.length);let[u,h]=C.computeOutAndReduceShapes(l.shape,i),d=_.sizeFromShape(u),p=_.makeZerosTypedArray(d,"int32"),f=_.sizeFromShape(h),m=n.data.get(l.dataId).values;for(let y=0;y<p.length;++y){let A=y*f,g=m[A],x=0;for(let v=0;v<f;++v){let w=m[A+v];w<g&&(g=w,x=v)}p[y]=x}return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),n.makeTensorInfo(u,"int32",p)}var 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U=j-k,X=m.get(y,L,j,A);X>F&&(F=X,a?O=s?((y*r.inHeight+L)*r.inWidth+j)*r.inChannels+A:(L*r.inWidth+j)*r.inChannels+A:O=V*d+U)}}i.set(O,y,g,b,A)}}return i}function T_(e,t,n,r,a,s){let i=a.strideDepth,o=a.strideHeight,l=a.strideWidth,c=a.dilationDepth,u=a.dilationHeight,h=a.dilationWidth,d=a.effectiveFilterDepth,p=a.effectiveFilterHeight,f=a.effectiveFilterWidth,m=a.padInfo.front,y=a.padInfo.top,A=a.padInfo.left,g=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,x=Be(a.outShape,n),v=x.values,w=a.outShape[1]*a.outShape[2]*a.outShape[3]*a.outShape[4],b=a.outShape[2]*a.outShape[3]*a.outShape[4],k=a.outShape[3]*a.outShape[4],N=a.outShape[4];for(let E=0;E<a.batchSize;++E){let F=E*w,O=E*r[0];for(let L=0;L<a.inChannels;++L)for(let V=0;V<a.outDepth;++V){let j=V*i-m,U=j;for(;U<0;)U+=c;let X=Math.min(a.inDepth,d+j),G=F+V*b;for(let ee=0;ee<a.outHeight;++ee){let Y=ee*o-y,ae=Y;for(;ae<0;)ae+=u;let te=Math.min(a.inHeight,p+Y),ie=G+ee*k;for(let Q=0;Q<a.outWidth;++Q){let he=Q*l-A,oe=he;for(;oe<0;)oe+=h;let fe=Math.min(a.inWidth,f+he),pe=ie+Q*N,ke=g,Se=0,Fe=0;for(let $e=U;$e<X;$e+=c){let et=O+$e*r[1];for(let tt=ae;tt<te;tt+=u){let it=et+tt*r[2];for(let Ke=oe;Ke<fe;Ke+=h){let dt=it+Ke*r[3],je=e[dt+L];if(s==="max"&&je>ke?ke=je:s==="avg"&&(Se+=je,Fe++),isNaN(ke))break}if(isNaN(ke))break}if(isNaN(ke))break}let Oe=pe+L;v[Oe]=s==="avg"?Se/Fe:ke}}}}return x}function C$(e,t){let n=Be(t.outShape,"int32"),r=t.strideDepth,a=t.strideHeight,s=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,h=t.effectiveFilterWidth,d=t.padInfo.front,p=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let y=0;y<t.inChannels;++y)for(let A=0;A<t.outDepth;++A){let g=A*r-d,x=g;for(;x<0;)x+=i;let v=Math.min(t.inDepth,c+g);for(let w=0;w<t.outHeight;++w){let b=w*a-p,k=b;for(;k<0;)k+=o;let N=Math.min(t.inHeight,u+b);for(let E=0;E<t.outWidth;++E){let F=E*s-f,O=F;for(;O<0;)O+=l;let L=Math.min(t.inWidth,h+F),V=Number.NEGATIVE_INFINITY,j=-1;for(let U=x;U<v;U+=i){let X=U-g;for(let G=k;G<N;G+=o){let ee=G-b;for(let Y=O;Y<L;Y+=l){let ae=Y-F,te=e.get(m,U,G,Y,y);te>=V&&(V=te,j=X*u*h+ee*u+ae)}}}n.set(j,m,A,w,E,y)}}}return n}function R$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;ve(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l),h;if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))h=Gr({inputs:{x:a},backend:n});else{let d=n.data.get(a.dataId).values,p=_.computeStrides(a.shape),f=uy(d,a.shape,a.dtype,p,u,"avg");h=n.makeTensorInfo(u.outShape,a.dtype,f.values)}return h}var M$={kernelName:fs,backendName:"cpu",kernelFunc:R$};function F$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r;ve(a,"avgPool3d");let u=C.computePool3DInfo(a.shape,s,i,1,o,l,c),h=n.data.get(a.dataId).values,d=T_(h,a.shape,a.dtype,_.computeStrides(a.shape),u,"avg");return n.makeTensorInfo(d.shape,"float32",d.values)}var $$={kernelName:ku,backendName:"cpu",kernelFunc:F$};function D$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,{filterSize:i,strides:o,pad:l,dimRoundingMode:c}=r;ve([a,s],"avgPool3DGrad");let u=C.computePool3DInfo(s.shape,i,o,1,l,c),h=u.strideDepth,d=u.strideHeight,p=u.strideWidth,f=u.filterDepth,m=u.filterHeight,y=u.filterWidth,A=u.dilationDepth,g=u.dilationHeight,x=u.dilationWidth,v=u.effectiveFilterDepth,w=u.effectiveFilterHeight,b=u.effectiveFilterWidth,k=v-1-u.padInfo.front,N=b-1-u.padInfo.left,E=w-1-u.padInfo.top,F=Be(s.shape,"float32"),O=1/(f*m*y),L=n.bufferSync(a);for(let V=0;V<u.batchSize;++V)for(let j=0;j<u.inChannels;++j)for(let U=0;U<u.inDepth;++U)for(let X=0;X<u.inHeight;++X)for(let G=0;G<u.inWidth;++G){let ee=U-k,Y=X-E,ae=G-N,te=0;for(let ie=0;ie<v;ie+=A){let Q=(ee+ie)/h;if(!(Q<0||Q>=u.outDepth||Math.floor(Q)!==Q))for(let he=0;he<w;he+=g){let oe=(Y+he)/d;if(!(oe<0||oe>=u.outHeight||Math.floor(oe)!==oe))for(let fe=0;fe<b;fe+=x){let pe=(ae+fe)/p;pe<0||pe>=u.outWidth||Math.floor(pe)!==pe||(te+=L.get(V,Q,oe,pe,j))}}}F.set(te*O,V,U,X,G,j)}return n.makeTensorInfo(F.shape,F.dtype,F.values)}var O$={kernelName:Oh,backendName:"cpu",kernelFunc:D$};function z$(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;ve([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=u.strideHeight,d=u.strideWidth,p=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,y=u.dilationWidth,A=u.effectiveFilterHeight,g=u.effectiveFilterWidth,x=g-1-u.padInfo.left,v=A-1-u.padInfo.top,w=Be(i.shape,"float32"),b=1/(p*f),k=n.data.get(a.dataId).values,N=Be(a.shape,"float32",k);for(let E=0;E<u.batchSize;++E)for(let F=0;F<u.inChannels;++F)for(let O=0;O<u.inHeight;++O)for(let L=0;L<u.inWidth;++L){let V=O-v,j=L-x,U=0;for(let X=0;X<A;X+=m){let G=(V+X)/h;if(!(G<0||G>=u.outHeight||Math.floor(G)!==G))for(let ee=0;ee<g;ee+=y){let Y=(j+ee)/d;Y<0||Y>=u.outWidth||Math.floor(Y)!==Y||(U+=N.get(E,G,Y,F))}}w.set(U*b,E,O,L,F)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var P$={kernelName:Dh,backendName:"cpu",kernelFunc:z$};function L$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:i,mean:o,variance:l}=t;_.assert(o.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ve([a,o,l,s,i],"batchNorm");let{varianceEpsilon:c}=r;c==null&&(c=.001);let u=n.data.get(a.dataId).values,h=n.data.get(o.dataId).values,d=n.data.get(l.dataId).values,p=s?n.data.get(s.dataId).values:new Float32Array([1]),f=i?n.data.get(i.dataId).values:new Float32Array([0]),m=new Float32Array(u.length),y=f.length,A=p.length,g=d.length,x=h.length,v=0,w=0,b=0,k=0;for(let N=0;N<u.length;++N)m[N]=f[v++]+(u[N]-h[w++])*p[b++]/Math.sqrt(d[k++]+c),v>=y&&(v=0),w>=x&&(w=0),b>=A&&(b=0),k>=g&&(k=0);return n.makeTensorInfo(a.shape,a.dtype,m)}var W$={kernelName:Ns,backendName:"cpu",kernelFunc:L$};function B$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;ve([a],"batchToSpaceND");let o=s.reduce((A,g)=>A*g),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=mt({inputs:{x:a},backend:n,attrs:{shape:l}}),f=nr({inputs:{x:p},backend:n,attrs:{perm:c}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:u}}),y=Ii({inputs:{x:m},backend:n,attrs:{begin:h,size:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),y}var V$={kernelName:Iu,backendName:"cpu",kernelFunc:B$};function j$(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=ty(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var U$={kernelName:zh,backendName:"cpu",kernelFunc:j$},H$=at(Ra,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),G$={kernelName:Ra,backendName:"cpu",kernelFunc:H$},q$=e=>{let{x:t}=e.inputs,n=e.backend,r=new Float32Array(_.sizeFromShape(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,i=a.complexTensorInfos.imag,o=n.data.get(s.dataId).values,l=n.data.get(i.dataId).values;for(let c=0;c<o.length;c++){let u=o[c],h=l[c];r[c]=Math.hypot(u,h)}return n.makeOutput(r,t.shape,"float32")},X$={kernelName:Su,backendName:"cpu",kernelFunc:q$};function Rl(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}var K$={kernelName:Jh,backendName:"cpu",kernelFunc:Rl};function Ml(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(m=>m.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>_.sizeFromShape(m.shape)>0);if(o.length===1)return Gr({inputs:{x:o[0]},backend:n});let l=o.map(m=>m.shape);if(C.assertParamsConsistent(l,s),o[0].dtype==="complex64"){let m=o.map(v=>ki({inputs:{input:v},backend:n})),y=o.map(v=>Rl({inputs:{input:v},backend:n})),A=Ml({inputs:m,backend:n,attrs:{axis:s}}),g=Ml({inputs:y,backend:n,attrs:{axis:s}}),x=Bn({inputs:{real:A,imag:g},backend:n});return m.forEach(v=>n.disposeIntermediateTensorInfo(v)),y.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(g),x}let c=o.map(m=>{let y=_.sizeFromShape(m.shape.slice(s));return mt({inputs:{x:m},backend:n,attrs:{shape:[-1,y]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));i=C.computeOutShape(c.map(m=>m.shape),1);let h=c[0].shape[0]===1,d=ny(u,i,t[0].dtype,h),p=C.computeOutShape(o.map(m=>m.shape),s),f=n.makeTensorInfo(p,t[0].dtype,d);return 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F=Math.max(0,Math.ceil((v-E)/p)),O=Math.min(d.outHeight,(d.inHeight+v-E)/p);for(let L=0;L<y;++L){let V=Math.max(0,Math.ceil((x-L)/f)),j=Math.min(d.outWidth,(d.inWidth+x-L)/f);for(let U=0;U<d.inChannels;++U)for(let X=0;X<d.outChannels;++X){let G=0;for(let ee=0;ee<d.batchSize;++ee)for(let Y=F;Y<O;++Y){let ae=E+Y*p-v;for(let te=V;te<j;++te){let ie=L+te*f-x;A?G+=k.get(ee,ae,ie,U)*N.get(ee,Y,te,X):G+=k.get(ee,U,ae,ie)*N.get(ee,X,Y,te)}}g.set(G,E,L,U,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var Q$={kernelName:Lh,backendName:"cpu",kernelFunc:J$};function eD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r;ve([a,s],"conv2dBackpropInput");let h=_.computeStrides(s.shape),d=_.computeStrides(a.shape),p=C.convertConv2DDataFormat(c),f=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,p),m=new 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tD={kernelName:xs,backendName:"cpu",kernelFunc:eD};function nD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r;ve([a,s],"conv3d");let c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:u,filterHeight:h,filterWidth:d,dilationDepth:p,dilationHeight:f,dilationWidth:m,padInfo:y}=c,A=y.front,g=y.left,x=y.top,v=new Dt(c.outShape,a.dtype),w=n.data.get(a.dataId).values,b=n.data.get(s.dataId).values,k=v.values,N=_.computeStrides(a.shape),E=_.computeStrides(s.shape);for(let F=0;F<c.batchSize;++F){let O=F*N[0],L=F*v.strides[0];for(let V=0;V<c.outDepth;++V){let j=L+V*v.strides[1],U=V*c.strideDepth-A;for(let X=0;X<u;++X){let G=U+X*p;if(G<0||G>=c.inDepth)continue;let ee=X*E[0],Y=O+G*N[1];for(let ae=0;ae<c.outHeight;++ae){let te=j+ae*v.strides[2],ie=ae*c.strideHeight-x;for(let Q=0;Q<h;++Q){let he=ie+Q*f;if(he<0||he>=c.inHeight)continue;let oe=ee+Q*E[1],fe=Y+he*N[2];for(let pe=0;pe<c.outWidth;++pe){let ke=te+pe*c.outChannels,Se=pe*c.strideWidth-g;for(let 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oe=0;oe<y;++oe){let fe=Math.max(0,Math.ceil((ae-oe)/p)),pe=Math.min(h.outHeight,(h.inHeight+ae-oe)/p),ke=oe*w+he;for(let Se=0;Se<A;++Se){let Fe=Math.max(0,Math.ceil((Y-Se)/f)),Oe=Math.min(h.outWidth,(h.inWidth+Y-Se)/f),$e=Se*b+ke;for(let et=0;et<h.inChannels;++et){let tt=et*k+$e;for(let it=0;it<h.outChannels;++it){let Ke=0;for(let dt=0;dt<h.batchSize;++dt){let je=dt*j,_n=dt*E;for(let bt=ie;bt<Q;++bt){let Kn=(te+bt*d-ee)*U+je,Xt=bt*F+_n;for(let vn=fe;vn<pe;++vn){let Zn=(oe+vn*p-ae)*X+Kn,Dn=vn*O+Xt;for(let on=Fe;on<Oe;++on){let Kt=(Se+on*f-Y)*G+Zn,Or=on*L+Dn;Ke+=V[Kt+et]*N[Or+it]}}}}x[tt+it]=Ke}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var sD={kernelName:Wh,backendName:"cpu",kernelFunc:aD};function iD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r;ve([a],"conv3dBackpropInputV2");let c=_.computeStrides(a.shape),u=_.computeStrides(s.shape),h=C.computeConv3DInfo(l,s.shape,o,1,i),d=new Dt(h.inShape,"float32"),p=d.values,[f,m,y,A]=d.strides,g=n.data.get(a.dataId).values,[x,v,w,b]=c,k=n.data.get(s.dataId).values,[N,E,F,O]=u,{batchSize:L,filterDepth:V,filterHeight:j,filterWidth:U,inChannels:X,inDepth:G,inHeight:ee,inWidth:Y,outChannels:ae,outDepth:te,outHeight:ie,outWidth:Q,strideDepth:he,strideHeight:oe,strideWidth:fe}=h,pe=V-1-h.padInfo.front,ke=j-1-h.padInfo.top,Se=U-1-h.padInfo.left;for(let Fe=0;Fe<L;++Fe)for(let Oe=0;Oe<X;++Oe)for(let $e=0;$e<G;++$e){let et=$e-pe,tt=Math.max(0,Math.ceil(et/he)),it=Math.min(te,(V+et)/he);for(let Ke=0;Ke<ee;++Ke){let dt=Ke-ke,je=Math.max(0,Math.ceil(dt/oe)),_n=Math.min(ie,(j+dt)/oe);for(let bt=0;bt<Y;++bt){let Kn=bt-Se,Xt=Math.max(0,Math.ceil(Kn/fe)),vn=Math.min(Q,(U+Kn)/fe),Zn=0;for(let Dn=tt;Dn<it;++Dn){let on=Dn*he-et;for(let Kt=je;Kt<_n;++Kt){let Or=Kt*oe-dt;for(let or=Xt;or<vn;++or){let lr=or*fe-Kn,ba=x*Fe+v*Dn+w*Kt+b*or,na=N*(V-1-on)+E*(j-1-Or)+F*(U-1-lr)+O*Oe;for(let _a=0;_a<ae;++_a){let 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ee=Math.floor(G),Y=Math.ceil(G),ae=G-ee;for(let te=0;te<y;te++){let ie=y>1?F*(d-1)+te*U:.5*(F+L)*(d-1);if(ie<0||ie>d-1){for(let fe=0;fe<p;fe++){let pe=fe+te*b[2]+X*b[1]+k*b[0];A.values[pe]=c}continue}let Q=Math.floor(ie),he=Math.ceil(ie),oe=ie-Q;for(let fe=0;fe<p;fe++){let pe=fe+Q*w[2]+ee*w[1]+V*w[0],ke=v[pe];pe=fe+he*w[2]+ee*w[1]+V*w[0];let Se=v[pe];pe=fe+Q*w[2]+Y*w[1]+V*w[0];let Fe=v[pe];pe=fe+he*w[2]+Y*w[1]+V*w[0];let Oe=v[pe],$e=ke+(Se-ke)*oe,et=Fe+(Oe-Fe)*oe;pe=fe+te*b[2]+X*b[1]+k*b[0],A.values[pe]=$e+(et-$e)*ae}}}else for(let ee=0;ee<y;++ee){let Y=y>1?F*(d-1)+ee*U:.5*(F+L)*(d-1);if(Y<0||Y>d-1){for(let ie=0;ie<p;ie++){let Q=ie+ee*b[2]+X*b[1]+k*b[0];A.values[Q]=c}continue}let ae=Math.round(Y),te=Math.round(G);for(let ie=0;ie<p;ie++){let Q=ie+ae*w[2]+te*w[1]+V*w[0],he=ie+ee*b[2]+X*b[1]+k*b[0];A.values[he]=v[Q]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var pD={kernelName:go,backendName:"cpu",kernelFunc:dD};function fD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r;ve(a,"cumsum");let l=C.getAxesPermutation([s],a.shape.length),c=a;l!=null&&(c=nr({inputs:{x:a},backend:n,attrs:{perm:l}}));let u=C.getInnerMostAxes(1,a.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let h=dr(c.dtype,"int32"),d=_.makeZerosTypedArray(_.sizeFromShape(c.shape),h),p=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=o?(A,g)=>A+f-g-1:(A,g)=>A+g;for(let A=0;A<p.length;A+=f)for(let g=0;g<f;g++){let x=m(A,g);if(g===0)d[x]=i?0:p[x];else{let v=m(A,g-1);d[x]=i?p[v]+d[v]:p[x]+d[v]}}let y=n.makeTensorInfo(c.shape,h,d);if(l!=null){let A=C.getUndoAxesPermutation(l),g=nr({inputs:{x:y},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(c),g}return y}var mD={kernelName:bs,backendName:"cpu",kernelFunc:fD};function yD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.data.get(a.dataId).values,c=n.data.get(s.dataId).values,u=ty(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=Zb(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var AD={kernelName:Vh,backendName:"cpu",kernelFunc:yD};function gD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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bD(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r;ve([a,s],"depthwiseConv2dNativeBackpropFilter");let h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),{strideHeight:d,strideWidth:p,filterHeight:f,filterWidth:m}=h,y=new Dt(h.filterShape,"float32"),A=h.padInfo.left,g=h.padInfo.top,x=h.outChannels/h.inChannels,v=n.data.get(a.dataId).values,w=new Dt(a.shape,a.dtype,v),b=n.data.get(s.dataId).values,k=new Dt(s.shape,s.dtype,b);for(let N=0;N<f;++N){let E=Math.max(0,Math.ceil((g-N)/d)),F=Math.min(h.outHeight,(h.inHeight+g-N)/d);for(let O=0;O<m;++O){let L=Math.max(0,Math.ceil((A-O)/p)),V=Math.min(h.outWidth,(h.inWidth+A-O)/p);for(let j=0;j<h.outChannels;++j){let U=Math.trunc(j/x),X=j%x,G=0;for(let ee=0;ee<h.batchSize;++ee)for(let Y=E;Y<F;++Y){let ae=N+Y*d-g;for(let te=L;te<V;++te){let ie=O+te*p-A;G+=w.get(ee,ae,ie,U)*k.get(ee,Y,te,j)}}y.set(G,N,O,U,X)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var _D={kernelName:jh,backendName:"cpu",kernelFunc:bD};function vD(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r;ve([a,s],"depthwiseConv2DNativeBackpropInput");let h=_.computeStrides(a.shape),d=_.computeStrides(s.shape),p=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),f=new Dt(p.inShape,"float32"),m=f.values,[y,A,g]=f.strides,x=n.data.get(a.dataId).values,[v,w,b]=h,k=n.data.get(s.dataId).values,[N,E,F]=d,{batchSize:O,filterHeight:L,filterWidth:V,inChannels:j,inHeight:U,inWidth:X,outChannels:G,outHeight:ee,outWidth:Y,strideHeight:ae,strideWidth:te}=p,ie=L-1-p.padInfo.top,Q=V-1-p.padInfo.left,he=G/j;for(let oe=0;oe<O;++oe)for(let fe=0;fe<j;++fe)for(let pe=0;pe<U;++pe){let ke=pe-ie,Se=Math.max(0,Math.ceil(ke/ae)),Fe=Math.min(ee,(L+ke)/ae);for(let Oe=0;Oe<X;++Oe){let $e=Oe-Q,et=Math.max(0,Math.ceil($e/te)),tt=Math.min(Y,(V+$e)/te),it=0;for(let Ke=Se;Ke<Fe;++Ke){let dt=Ke*ae-ke;for(let je=et;je<tt;++je){let 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SD={kernelName:Hh,backendName:"cpu",kernelFunc:ID},ND={kernelName:Tu,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a}=e,{strides:s,pad:i,dilations:o}=n,l=t,c=l.data.get(r.dataId).values,u=r.shape.length,h=l.data.get(a.dataId).values,d=a.shape.length,{batchSize:p,inHeight:f,inWidth:m,inChannels:y,outHeight:A,outWidth:g,padInfo:x,strideHeight:v,strideWidth:w,filterHeight:b,filterWidth:k,dilationHeight:N,dilationWidth:E,outShape:F}=C.computeDilation2DInfo(r.shape,a.shape,s,i,"NHWC",o),O=_.sizeFromShape(F),L=F.length,V=_.getArrayFromDType(r.dtype,O);for(let j=0;j<p;++j)for(let U=0;U<A;++U){let X=U*v-x.top;for(let G=0;G<g;++G){let ee=G*w-x.left;for(let Y=0;Y<y;++Y){let ae=Number.MIN_SAFE_INTEGER;for(let ie=0;ie<b;++ie){let Q=X+ie*N;if(Q>=0&&Q<f)for(let he=0;he<k;++he){let oe=ee+he*E;if(oe>=0&&oe<m){let fe=_.locToIndex([j,Q,oe,Y],u,_.computeStrides(r.shape)),pe=_.locToIndex([ie,he,Y],d,_.computeStrides(a.shape)),ke=c[fe]+h[pe];ke>ae&&(ae=ke)}}}let te=_.locToIndex([j,U,G,Y],L,_.computeStrides(F));V[te]=ae}}}return{dataId:l.write(_.toTypedArray(V,r.dtype),F,r.dtype),shape:F,dtype:r.dtype}}},TD={kernelName:qh,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:r,filter:a,dy:s}=e,{strides:i,pad:o,dilations:l}=n,c=t,u=_.toNestedArray(r.shape,c.data.get(r.dataId).values),h=_.toNestedArray(a.shape,c.data.get(a.dataId).values),{batchSize:d,inHeight:p,inWidth:f,inChannels:m,outHeight:y,outWidth:A,padInfo:g,strideHeight:x,strideWidth:v,filterHeight:w,filterWidth:b,dilationHeight:k,dilationWidth:N,outShape:E}=C.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",l);_.assert(s.rank===E.length,()=>`Error in ${qh}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let F=_.toNestedArray(E,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(a.shape,a.dtype);for(let L=0;L<d;++L)for(let V=0;V<y;++V){let j=V*x-g.top;for(let U=0;U<A;++U){let X=U*v-g.left;for(let G=0;G<m;++G){let 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F=_.toNestedArray(E,c.data.get(s.dataId).values),O=_.makeZerosNestedTypedArray(r.shape,r.dtype);for(let L=0;L<d;++L)for(let V=0;V<y;++V){let j=V*x-g.top;for(let U=0;U<A;++U){let X=U*v-g.left;for(let G=0;G<m;++G){let ee=Number.MIN_SAFE_INTEGER,Y=j<0?0:j,ae=X<0?0:X;for(let te=0;te<w;++te){let ie=j+te*k;if(ie>=0&&ie<p)for(let Q=0;Q<b;++Q){let he=X+Q*N;if(he>=0&&he<f){let oe=u[L][ie][he][G]+h[te][Q][G];oe>ee&&(ee=oe,Y=ie,ae=he)}}}O[L][Y][ae][G]+=F[L][V][U][G]}}}return{dataId:c.write(_.toTypedArray(O,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function fc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;ve(a,"sum");let o;a.dtype==="bool"?o=Ha({inputs:{x:a},backend:n,attrs:{dtype:"int32"}}):o=Gr({inputs:{x:a},backend:n});let l=o.shape.length,c=_.parseAxisParam(s,o.shape),u=C.getAxesPermutation(c,l),h=c,d=o;u!=null&&(d=nr({inputs:{x:o},backend:n,attrs:{perm:u}}),h=C.getInnerMostAxes(h.length,l)),C.assertAxesAreInnerMostDims("sum",h,d.shape.length);let[p,f]=C.computeOutAndReduceShapes(d.shape,h),m=C.upcastType(d.dtype,"int32"),y=up(n,p,m),A=_.sizeFromShape(f),g=n.data.get(y.dataId).values,x=n.data.get(d.dataId).values;for(let v=0;v<g.length;++v){let w=v*A,b=0;for(let k=0;k<A;++k)b+=x[w+k];g[v]=b}if(i){let v=C.expandShapeToKeepDim(y.shape,c),w=y;y=mt({inputs:{x:y},backend:n,attrs:{shape:v}}),n.disposeIntermediateTensorInfo(w)}return n.disposeIntermediateTensorInfo(o),u!=null&&n.disposeIntermediateTensorInfo(d),y}var CD={kernelName:Qs,backendName:"cpu",kernelFunc:fc};function RD(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=C.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,f=[];for(let m=0;m<h;++m){for(let y of u[m]){let{permutationIndices:A,expandDims:g}=C.getEinsumPermutation(p,l[y]),x;C.isIdentityPermutation(A)?x=s[y]:(x=nr({inputs:{x:s[y]},backend:n,attrs:{perm:A}}),f.push(x));let v=x.shape.slice();for(let w=0;w<g.length;++w)v.splice(g[w],0,1);_.arraysEqual(x.shape,v)||(x=mt({inputs:{x},backend:n,attrs:{shape:v}}),f.push(x)),d===null?d=x:(d=cp({inputs:{a:x,b:d},backend:n}),f.push(d))}m<h-1&&(c[m]>=0&&(d=fc({inputs:{x:d},backend:n,attrs:{axis:c[m]-(i.length-p),keepDims:!1}}),f.push(d)),p--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var MD={kernelName:Xh,backendName:"cpu",kernelFunc:RD};function FD(e){let{inputs:t,backend:n}=e,{dy:r,y:a}=t;ve([r,a],"eluGrad");let s=new Float32Array(_.sizeFromShape(a.shape)),i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values;for(let l=0;l<i.length;++l){let c=i[l];c>=1?s[l]=o[l]:s[l]=o[l]*(c+1)}return n.makeTensorInfo(a.shape,"float32",s)}var $D={kernelName:Kh,backendName:"cpu",kernelFunc:FD},DD=Mt((e,t)=>e===t?1:0),R_=Ut(_o,DD,null,"bool"),OD={kernelName:_o,backendName:"cpu",kernelFunc:R_},zD=C.ERF_P,PD=C.ERF_A1,LD=C.ERF_A2,WD=C.ERF_A3,BD=C.ERF_A4,VD=C.ERF_A5,jD=at(bo,e=>{let t=Math.sign(e),n=Math.abs(e),r=1/(1+zD*n);return t*(1-((((VD*r+BD)*r+WD)*r+LD)*r+PD)*r*Math.exp(-n*n))}),UD={kernelName:bo,backendName:"cpu",kernelFunc:jD};function hp(e){let{inputs:t,backend:n,attrs:r}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(_.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),mt({inputs:{x:a},backend:n,attrs:{shape:o}})}var HD={kernelName:vo,backendName:"cpu",kernelFunc:hp},GD=Mt((e,t)=>e/t),cy=Ut(vs,GD),hy={kernelName:vs,backendName:"cpu",kernelFunc:cy};function M_(e,t,n){let r=e.shape,a=r[0],s=r[1],i=n.data.get(e.dataId),o=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,c=[a,s],u=_.sizeFromShape(c),h=_.getTypedArrayFromDType("float32",u),d=_.getTypedArrayFromDType("float32",u);for(let y=0;y<a;y++){let A=Ii({inputs:{x:o},backend:n,attrs:{begin:[y,0],size:[1,s]}}),g=Ii({inputs:{x:l},backend:n,attrs:{begin:[y,0],size:[1,s]}}),x=Bn({inputs:{real:A,imag:g},backend:n}),{real:v,imag:w}=qD(x,t,n),b=C.mergeRealAndImagArrays(v,w);for(let k=0;k<s;k++){let N=C.getComplexWithIndex(b,k);h[y*s+k]=N.real,d[y*s+k]=N.imag}n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(x)}let p=n.makeTensorInfo(c,"float32",h),f=n.makeTensorInfo(c,"float32",d),m=Bn({inputs:{real:p,imag:f},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),m}function qD(e,t,n){let r=_.sizeFromShape(e.shape),a=n.data.get(e.dataId),s=n.data.get(a.complexTensorInfos.real.dataId).values,i=n.data.get(a.complexTensorInfos.imag.dataId).values;if(XD(r)){let o=dy(s,i,r,t,n),l=[e.shape[0],e.shape[1]];if(t){let c=n.makeTensorInfo(l,"float32",o.real),u=n.makeTensorInfo(l,"float32",o.imag),h=n.makeTensorInfo([],"float32",_.createScalarValue(r,"float32")),d=Gr({inputs:{x:h},backend:n}),p=hy.kernelFunc({inputs:{a:c,b:h},backend:n}),f=hy.kernelFunc({inputs:{a:u,b:d},backend:n}),m=n.data.get(p.dataId).values,y=n.data.get(f.dataId).values;return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),{real:m,imag:y}}return o}else{let o=C.mergeRealAndImagArrays(s,i),l=KD(o,r,t);return C.splitRealAndImagArrays(l)}}function XD(e){return(e&e-1)==0}function dy(e,t,n,r,a){if(n===1)return{real:e,imag:t};let s=C.mergeRealAndImagArrays(e,t),i=n/2,o=C.complexWithEvenIndex(s),l=o.real,c=o.imag,u=[l.length],h=a.makeTensorInfo(u,"float32",l),d=a.makeTensorInfo(u,"float32",c),p=Bn({inputs:{real:h,imag:d},backend:a}),f=C.complexWithOddIndex(s),m=f.real,y=f.imag,A=[m.length],g=a.makeTensorInfo(A,"float32",m),x=a.makeTensorInfo(A,"float32",y),v=Bn({inputs:{real:g,imag:x},backend:a}),w=dy(l,c,i,r,a),b=w.real,k=w.imag,N=[b.length],E=a.makeTensorInfo(N,"float32",b),F=a.makeTensorInfo(N,"float32",k),O=Bn({inputs:{real:E,imag:F},backend:a}),L=dy(m,y,i,r,a),V=L.real,j=L.imag,U=[V.length],X=a.makeTensorInfo(U,"float32",V),G=a.makeTensorInfo(U,"float32",j),ee=Bn({inputs:{real:X,imag:G},backend:a}),Y=C.exponents(n,r),ae=[Y.real.length],te=a.makeTensorInfo(ae,"float32",Y.real),ie=a.makeTensorInfo(ae,"float32",Y.imag),Q=Bn({inputs:{real:te,imag:ie},backend:a}),he=cp({inputs:{a:Q,b:ee},backend:a}),oe=pc({inputs:{a:O,b:he},backend:a}),fe=oy({inputs:{a:O,b:he},backend:a}),pe=ki({inputs:{input:oe},backend:a}),ke=ki({inputs:{input:fe},backend:a}),Se=Rl({inputs:{input:oe},backend:a}),Fe=Rl({inputs:{input:fe},backend:a}),Oe=Ml({inputs:[pe,ke],backend:a,attrs:{axis:0}}),$e=Ml({inputs:[Se,Fe],backend:a,attrs:{axis:0}}),et=a.data.get(Oe.dataId).values,tt=a.data.get($e.dataId).values;return a.disposeIntermediateTensorInfo(h),a.disposeIntermediateTensorInfo(d),a.disposeIntermediateTensorInfo(p),a.disposeIntermediateTensorInfo(g),a.disposeIntermediateTensorInfo(x),a.disposeIntermediateTensorInfo(v),a.disposeIntermediateTensorInfo(E),a.disposeIntermediateTensorInfo(F),a.disposeIntermediateTensorInfo(O),a.disposeIntermediateTensorInfo(X),a.disposeIntermediateTensorInfo(G),a.disposeIntermediateTensorInfo(ee),a.disposeIntermediateTensorInfo(te),a.disposeIntermediateTensorInfo(ie),a.disposeIntermediateTensorInfo(Q),a.disposeIntermediateTensorInfo(he),a.disposeIntermediateTensorInfo(oe),a.disposeIntermediateTensorInfo(fe),a.disposeIntermediateTensorInfo(pe),a.disposeIntermediateTensorInfo(Se),a.disposeIntermediateTensorInfo(ke),a.disposeIntermediateTensorInfo(Fe),a.disposeIntermediateTensorInfo(Oe),a.disposeIntermediateTensorInfo($e),{real:et,imag:tt}}function KD(e,t,n){let r=new Float32Array(t*2);for(let a=0;a<t;a++){let s=0,i=0;for(let o=0;o<t;o++){let 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Ce=J();Ce.registerFlag("HAS_WEBGL",()=>Ce.getNumber("WEBGL_VERSION")>0);Ce.registerFlag("WEBGL_VERSION",()=>aA(2)?2:aA(1)?1:0);Ce.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ce.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ce.get("WEBGL_VERSION")===2);Ce.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ce.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ce.registerFlag("WEBGL_PACK",()=>Ce.getBool("HAS_WEBGL"));Ce.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_CLIP",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Ce.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_PACK_REDUCE",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_CONV_IM2COL",()=>Ce.getBool("WEBGL_PACK"));Ce.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>V_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>j_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ce.getNumber("WEBGL_VERSION");return e===0?0:U_(e)});Ce.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ce.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Vu.isMobile());Ce.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>H_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ce.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ce.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ce.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>G_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_FENCE_API_ENABLED",()=>q_(Ce.getNumber("WEBGL_VERSION")));Ce.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ce.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ce.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ce.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Vu.isMobile()&&Ce.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function dn(){let e,t,n,r,a,s,i,o,l,c;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
+`)[0]),console.log(`%c ${_.rightPad(c[0],o)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(u.join(`
+`))}function G_(e){return ma(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}function q_(e,t){if(we(e,()=>e.linkProgram(t)),e.getProgramParameter(t,e.LINK_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function pp(e,t){if(we(e,()=>e.validateProgram(t)),e.getProgramParameter(t,e.VALIDATE_STATUS)===!1)throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function X_(e,t){let n=ma(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),we(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function K_(e,t){let n=ma(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),we(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}function XP(){return J().getNumber("WEBGL_VERSION")===2?1:4}function Z_(e){return ma(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}function Y_(e,t){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){let r=`[${e}x${t}]`;throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){let r=`[${e}x${t}]`,a=`[${n}x${n}]`;throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+a+".")}}function J_(e){return ma(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function fy(e,t,n,r,a,s,i){let o=e.getAttribLocation(t,n);return o===-1?!1:(we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),we(e,()=>e.vertexAttribPointer(o,a,e.FLOAT,!1,s,i)),we(e,()=>e.enableVertexAttribArray(o)),!0)}function Q_(e,t,n){h3(e,n),we(e,()=>e.activeTexture(e.TEXTURE0+n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,t))}function KP(e,t){h3(e,t),we(e,()=>e.activeTexture(e.TEXTURE0+t)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function e3(e,t,n){return ma(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}function t3(e,t,n){return e.getUniformLocation(t,n)}function n3(e,t,n,r){we(e,()=>Q_(e,t,r)),we(e,()=>e.uniform1i(n,r))}function ZP(e){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.viewport(0,0,e.canvas.width,e.canvas.height)),we(e,()=>e.scissor(0,0,e.canvas.width,e.canvas.height))}function fp(e,t,n){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,n)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0))}function my(e,t){we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,t)),we(e,()=>e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0))}function Ac(e){let t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+r3(e,t))}function r3(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function ma(e,t,n){let r=we(e,()=>t());if(r==null)throw new Error(n);return r}function h3(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n){let a=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${a}.`)}}function Si(e,t=2){return _.sizeFromShape(e.slice(0,e.length-t))}function Ni(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function mp(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[Si(e),...Ni(e)]),t}function a3(e,t=!1){let n=J().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((a,s)=>s>=e.length-2?_.nearestLargerEven(e[s]):e[s]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=_.squeezeShape(e).newShape);let r=_.sizeFromShape(e);if(e.length<=1&&r<=n)return[1,r];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let a=Si(e),s=2,i=2;return e.length&&([s,i]=Ni(e)),r=a*(s/2)*(i/2),_.sizeToSquarishShape(r).map(o=>o*2)}return _.sizeToSquarishShape(r)}function Ap(e){return e%2==0}function gc(e,t){if(e=e.slice(-2),t=t.slice(-2),_.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r||Ap(n)&&Ap(r)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Ap(e[0])&&Ap(t[0])}var gp,xp;function s3(e){if(gp==null){let t=qr(e);gp=t.getParameter(t.MAX_TEXTURE_SIZE)}return gp}function YP(){gp=null}function JP(){xp=null}function i3(e){if(xp==null){let t=qr(e);xp=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,xp)}function o3(e){if(e===0)return 0;let t,n=qr(e);return rr(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:rr(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function rr(e,t){return e.getExtension(t)!=null}function yy(e){try{if(qr(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function l3(e){if(e===0)return!1;let t=qr(e);if(e===1){if(!rr(t,"OES_texture_float"))return!1}else if(!rr(t,"EXT_color_buffer_float"))return!1;return xy(t)}function u3(e){if(e===0)return!1;let t=qr(e);if(e===1){if(!rr(t,"OES_texture_float")||!rr(t,"WEBGL_color_buffer_float"))return!1}else{if(rr(t,"EXT_color_buffer_float"))return xy(t);let n="EXT_color_buffer_half_float";if(rr(t,n)){let r=t.getExtension(n);return lL(t,r)}return!1}return xy(t)}function xy(e){let t=gy(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,r,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(s),i}function lL(e,t){let n=gy(e,t),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,a,s,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(i),o}function c3(e){return e!==2?!1:qr(e).fenceSync!=null}function Fl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&_.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Ee=J();Ee.registerFlag("HAS_WEBGL",()=>Ee.getNumber("WEBGL_VERSION")>0);Ee.registerFlag("WEBGL_VERSION",()=>yy(2)?2:yy(1)?1:0);Ee.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ee.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ee.get("WEBGL_VERSION")===2);Ee.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ee.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ee.registerFlag("WEBGL_PACK",()=>Ee.getBool("HAS_WEBGL"));Ee.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_CLIP",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!0);Ee.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_PACK_REDUCE",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_CONV_IM2COL",()=>Ee.getBool("WEBGL_PACK"));Ee.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>s3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>i3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Ee.getNumber("WEBGL_VERSION");return e===0?0:o3(e)});Ee.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Ee.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!qu.isMobile());Ee.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>l3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Ee.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Ee.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Ee.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>u3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_FENCE_API_ENABLED",()=>c3(Ee.getNumber("WEBGL_VERSION")));Ee.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Ee.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Ee.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ee.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>qu.isMobile()&&Ee.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});function dn(){let e,t,n,r,a,s,i,o,l,c;return J().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",r="in",a="texture",s="outputColor",i="out vec4 outputColor;",o=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Ii(e,t,n="index"){let r=v.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function lA(e){let t=v.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:r,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:c}}function Ei(e,t,n="index"){let r=_.computeStrides(t);return r.map((a,s)=>{let i=`int ${e[s]} = ${n} / ${a}`,o=s===r.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function wy(e){let t=_.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}var K_=`
+`}var d3=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -117,9 +117,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,WP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=fc.DENSE;let t=Ac(e),n=dn();this.outputShape=e,this.userCode=`
+`,uL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=xc.DENSE;let t=bc(e),n=dn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Ii(["r","c","d"],e)}
+        ${Ei(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -138,9 +138,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},BP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=fc.DENSE;let t=Ac(e),n=dn();this.outputShape=e,this.userCode=`
+    `}},cL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xc.DENSE;let t=bc(e),n=dn();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${Ii(["r","c","d"],e)}
+        ${Ei(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -159,23 +159,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${n.output} = result;
       }
-    `}},VP=class{constructor(e){this.variableNames=["A"],this.outTexUsage=nr.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
-      ${K_}
+    `}},hL=class{constructor(e){this.variableNames=["A"],this.outTexUsage=ar.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
+      ${d3}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},jP=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=nr.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
-      ${K_}
+    `}},dL=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=ar.DOWNLOAD;let t=dn();this.outputShape=e,this.userCode=`
+      ${d3}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},UP=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=dn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${lA(e)}
+    `}},pL=class{constructor(e,t,n=!1){this.variableNames=["A"];let r=dn(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${wy(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -204,7 +204,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}},HP=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=dn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
+    `}},fL=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let r=dn(),[a,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let l=0;l<=1;l++)for(let c=0;c<=1;c++){let u=l*2+c;i+=`
           localCoords = coords;
           if(localCoords[2] + ${c} < ${e[2]}) {
             localCoords[2] += ${c};
@@ -233,7 +233,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
             }
           }
         `}this.userCode=`
-      ${lA(e)}
+      ${wy(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -248,7 +248,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${r.output} = ${o};
       }
-    `}},Z_={};Me(Z_,{bindVertexProgramAttributeStreams:()=>s3,createBufferFromOutputTexture:()=>l3,createFloat16MatrixTexture:()=>t3,createFloat16PackedMatrixTexture:()=>a3,createFloat32MatrixTexture:()=>e3,createIndexBuffer:()=>Q_,createPackedMatrixTexture:()=>r3,createUnsignedBytesMatrixTexture:()=>n3,createVertexBuffer:()=>J_,createVertexShader:()=>Y_,downloadByteEncodedFloatMatrixFromOutputTexture:()=>c3,downloadFloat32MatrixFromBuffer:()=>u3,downloadMatrixFromPackedOutputTexture:()=>d3,downloadPackedMatrixFromBuffer:()=>h3,getInternalFormatForFloat16MatrixTexture:()=>cA,getInternalFormatForFloat16PackedMatrixTexture:()=>pA,getInternalFormatForFloat32MatrixTexture:()=>uA,getInternalFormatForPackedMatrixTexture:()=>dA,getInternalFormatForUnsignedBytesMatrixTexture:()=>hA,uploadDenseMatrixToTexture:()=>i3,uploadPixelDataToTexture:()=>o3});function Y_(e){let t=dn(),n=`${t.version}
+    `}},p3={};Me(p3,{bindVertexProgramAttributeStreams:()=>_3,createBufferFromOutputTexture:()=>I3,createFloat16MatrixTexture:()=>g3,createFloat16PackedMatrixTexture:()=>b3,createFloat32MatrixTexture:()=>A3,createIndexBuffer:()=>y3,createPackedMatrixTexture:()=>w3,createUnsignedBytesMatrixTexture:()=>x3,createVertexBuffer:()=>m3,createVertexShader:()=>f3,downloadByteEncodedFloatMatrixFromOutputTexture:()=>N3,downloadFloat32MatrixFromBuffer:()=>S3,downloadMatrixFromPackedOutputTexture:()=>E3,downloadPackedMatrixFromBuffer:()=>T3,getInternalFormatForFloat16MatrixTexture:()=>_y,getInternalFormatForFloat16PackedMatrixTexture:()=>Iy,getInternalFormatForFloat32MatrixTexture:()=>by,getInternalFormatForPackedMatrixTexture:()=>ky,getInternalFormatForUnsignedBytesMatrixTexture:()=>vy,uploadDenseMatrixToTexture:()=>v3,uploadPixelDataToTexture:()=>k3});function f3(e){let t=dn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -257,22 +257,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return S_(e,n)}function J_(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return R_(e,t)}function Q_(e){let t=new Uint16Array([0,1,2,2,1,3]);return M_(e,t)}function yc(e,t,n,r,a,s){D_(t,n);let i=F_(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function uA(e){return e.internalFormatFloat}function e3(e,t,n,r){let[a,s]=mc(t,n);return yc(e,a,s,uA(r),r.textureFormatFloat,e.FLOAT)}function cA(e){return e.internalFormatHalfFloat}function t3(e,t,n,r){let[a,s]=mc(t,n);return yc(e,a,s,cA(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function hA(e){return e.downloadTextureFormat}function n3(e,t,n,r){let[a,s]=mc(t,n);return yc(e,a,s,hA(r),e.RGBA,e.UNSIGNED_BYTE)}function dA(e){return e.internalFormatPackedFloat}function r3(e,t,n,r){let[a,s]=El(t,n);return yc(e,a,s,dA(r),e.RGBA,e.FLOAT)}function pA(e){return e.internalFormatPackedHalfFloat}function a3(e,t,n,r){let[a,s]=El(t,n);return yc(e,a,s,pA(r),e.RGBA,r.textureTypeHalfFloat)}function s3(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),nA(e,t,"clipSpacePos",n,3,s,r)&&nA(e,t,"uv",n,2,s,a)}function i3(e,t,n,r,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function o3(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function l3(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function u3(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function c3(e,t,n,r){let[a,s]=mc(t,n),i=4,o=new Uint8Array(MP(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function h3(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(FP(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function d3(e,t,n){let r=new Float32Array(t*n*4);return we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var Ap=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,dp(t,e)):this.gl=qr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=hc(this.gl,a),tr(this.gl,s))this.textureHalfFloatExtension=hc(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),tr(this.gl,r))this.colorBufferHalfFloatExtension=hc(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",tr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(tr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=J_(this.gl),this.indexBuffer=Q_(this.gl),this.framebuffer=$_(this.gl),this.textureConfig=iA(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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+    }`;return U_(e,n)}function m3(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return X_(e,t)}function y3(e){let t=new Uint16Array([0,1,2,2,1,3]);return K_(e,t)}function _c(e,t,n,r,a,s){Y_(t,n);let i=Z_(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),we(e,()=>e.texImage2D(o,0,r,t,n,0,a,s,null)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function by(e){return e.internalFormatFloat}function A3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,by(r),r.textureFormatFloat,e.FLOAT)}function _y(e){return e.internalFormatHalfFloat}function g3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,_y(r),r.textureFormatFloat,r.textureTypeHalfFloat)}function vy(e){return e.downloadTextureFormat}function x3(e,t,n,r){let[a,s]=wc(t,n);return _c(e,a,s,vy(r),e.RGBA,e.UNSIGNED_BYTE)}function ky(e){return e.internalFormatPackedFloat}function w3(e,t,n,r){let[a,s]=$l(t,n);return _c(e,a,s,ky(r),e.RGBA,e.FLOAT)}function Iy(e){return e.internalFormatPackedHalfFloat}function b3(e,t,n,r){let[a,s]=$l(t,n);return _c(e,a,s,Iy(r),e.RGBA,r.textureTypeHalfFloat)}function _3(e,t,n){let r=0,a=3*4,s=3*4+2*4;return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),fy(e,t,"clipSpacePos",n,3,s,r)&&fy(e,t,"uv",n,2,s,a)}function v3(e,t,n,r,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function k3(e,t,n){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function I3(e,t,n,r){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*n;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function S3(e,t,n){let r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}function N3(e,t,n,r){let[a,s]=wc(t,n),i=4,o=new Uint8Array(tL(t*n,i));return we(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function T3(e,t,n,r,a,s,i,o){let l=e,c=new Float32Array(nL(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function E3(e,t,n){let r=new Float32Array(t*n*4);return we(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}var wp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=J().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,yp(t,e)):this.gl=qr(t);let n="WEBGL_color_buffer_float",r="EXT_color_buffer_half_float";if(J().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=yc(this.gl,a),rr(this.gl,s))this.textureHalfFloatExtension=yc(this.gl,s);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),rr(this.gl,r))this.colorBufferHalfFloatExtension=yc(this.gl,r);else if(J().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",rr(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(rr(this.gl,r))this.colorBufferHalfFloatExtension=this.gl.getExtension(r);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=m3(this.gl),this.indexBuffer=y3(this.gl),this.framebuffer=J_(this.gl),this.textureConfig=gy(this.gl,this.textureHalfFloatExtension)}get debug(){return J().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;we(e,()=>e.finish()),we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.deleteFramebuffer(this.framebuffer)),we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),we(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),A3(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),g3(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),x3(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),k3(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),v3(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),b3(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),w3(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(my(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>N3(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return T3(this.gl,e,t,n,r,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return S3(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let r=I3(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=r.clientWaitSync(a,0,0);return s===r.ALREADY_SIGNALED||s===r.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>E3(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=H_(t,e),r=f3(t),a=G_(t);return we(t,()=>t.attachShader(a,r)),we(t,()=>t.attachShader(a,n)),q_(t,a),this.debug&&pp(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=_3(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&pp(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?e3(this.gl,e,t):t3(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),n3(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[r,a]=$l(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&pp(this.gl,this.program),Ac(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),we(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),we(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=yc(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.createQuery();return n.beginQuery(r.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await _.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,r=this.getQueryTimerExtensionWebGL2(),a=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),r=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=mL(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&_.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),fp(this.gl,e,this.framebuffer),this.debug&&Ac(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(fp(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ac(this.gl)):my(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let r=this.gl;fp(r,e,this.framebuffer),this.debug&&Ac(r),this.outputTexture=e,we(r,()=>r.viewport(0,0,t,n)),we(r,()=>r.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,n,r))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function mL(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:C3}=C;function kL(e,t,n,r){let a=[];e.forEach(p=>{let f=_.sizeFromShape(p.shapeInfo.logicalShape);p.shapeInfo.isUniform?a.push(`uniform float ${p.name}${f>1?`[${f}]`:""};`):(a.push(`uniform sampler2D ${p.name};`),a.push(`uniform int offset${p.name};`))});let s=a.join(`
+`),i=e.map(p=>yL(p,t,r)).join(`
+`),o=t.texShape,l=dn(),c=xL(l),u,h,d=_L(l);return t.isPacked?(u=AL(t.logicalShape,o),h=bL(l)):(u=gL(t.logicalShape,o),h=wL(l)),r&&(d+=vL),[d,c,h,s,u,i,n].join(`
+`)}function Dl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return IL(e);case 1:return SL(e);case 2:return NL(e);case 3:return TL(e);case 4:return EL(e);case 5:return CL(e);case 6:return RL(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function R3(e){switch(e.shapeInfo.logicalShape.length){case 0:return ML(e);case 1:return FL(e);case 2:return $L(e);case 3:return DL(e);default:return OL(e)}}function yL(e,t,n=!1){let r="";n?r+=R3(e):r+=Dl(e);let a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(n?r+=zL(e,t):r+=PL(e,t)),r}function AL(e,t){switch(e.length){case 0:return M3();case 1:return LL(e,t);case 2:return VL(e,t);case 3:return WL(e,t);default:return BL(e,t)}}function gL(e,t){switch(e.length){case 0:return M3();case 1:return jL(e,t);case 2:return XL(e,t);case 3:return UL(e,t);case 4:return HL(e,t);case 5:return GL(e,t);case 6:return qL(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function xL(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function YP(e){return`
+  `}function wL(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function JP(e){return`
+  `}function bL(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function QP(e){return`${e.version}
+  `}function _L(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${NL}
-    ${SL}
-    ${TL}
-  `}var NL=`
+    ${KL}
+    ${ZL}
+    ${YL}
+  `}var KL=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,SL=`
+`,ZL=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,TL=`
+`,YL=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,eL=`
+`,vL=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -370,11 +370,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function m3(){return`
+`;function M3(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function AL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
+  `}function LL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ${n[1]}.0);
       }
@@ -388,7 +388,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function wL(e,t){return t[0]===1?`
+  `}function jL(e,t){return t[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${t[1]}.0);
       }
@@ -402,7 +402,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function yL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
+  `}function WL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${n[0]}, ${n[1]}));
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function bL(e,t){let n=Ii(["r","c","d"],e);return`
+  `}function UL(e,t){let n=Ei(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function gL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
+  `}function BL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),s=a,i="",o="b, r, c";for(let l=2;l<e.length-1;l++)s*=e[e.length-l-1],i=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+i,o=`b${l}, `+o;return`
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${o});
     }
-  `}function _L(e,t){let n=Ii(["r","c","d","d2"],e);return`
+  `}function HL(e,t){let n=Ei(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -451,7 +451,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function vL(e,t){let n=Ii(["r","c","d","d2","d3"],e);return`
+  `}function GL(e,t){let n=Ei(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function kL(e,t){let n=Ii(["r","c","d","d2","d3","d4"],e);return`
+  `}function qL(e,t){let n=Ei(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -474,7 +474,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function xL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return`
+  `}function VL(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(_.arraysEqual(e,t))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
       }
@@ -489,7 +489,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function IL(e,t){return v.arraysEqual(e,t)?`
+  `}function XL(e,t){return _.arraysEqual(e,t)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
@@ -516,34 +516,34 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Ni(e){return`offset${e}`}function uL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=dn();return`
+  `}function Ci(e){return`offset${e}`}function ML(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=dn();return`
     vec4 ${n}() {
       return ${r.texture2D}(${t}, halfCR);
     }
-  `}function nL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+  `}function IL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
       float ${n}() {
         return sampleTexture(${t}, halfCR);
       }
-    `;let[s,i]=e.shapeInfo.texShape,o=Ni(t);return`
+    `;let[s,i]=e.shapeInfo.texShape,o=Ci(t);return`
     float ${n}() {
       vec2 uv = uvFromFlat(${s}, ${i}, ${o});
       return sampleTexture(${t}, uv);
     }
-  `}function cL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=dn();return`
+  `}function FL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,a=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],s=dn();return`
     vec4 ${n}(int index) {
       vec2 uv = packedUVfrom1D(
         ${a[0]}, ${a[1]}, index);
       return ${s.texture2D}(${t}, uv);
     }
-  `}function rL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function SL(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let r=e.shapeInfo.texShape,a=r[0],s=r[1];if(s===1&&a===1)return`
       float ${n}(int index) {
         return sampleTexture(${t}, halfCR);
       }
-    `;let i=Ni(t);return s===1?`
+    `;let i=Ci(t);return s===1?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
         return sampleTexture(${t}, uv);
@@ -558,7 +558,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${a}, ${s}, index + ${i});
       return sampleTexture(${t}, uv);
     }
-  `}function hL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=dn();if(a!=null&&v.arraysEqual(t,a))return`
+  `}function $L(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=a[0],i=a[1],o=dn();if(a!=null&&_.arraysEqual(t,a))return`
       vec4 ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
 
@@ -569,22 +569,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function aL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(t,a)){let h=a[0],d=a[1];return`
+  `}function NL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape;if(a!=null&&_.arraysEqual(t,a)){let h=a[0],d=a[1];return`
     float ${r}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${d}.0, ${h}.0);
       return sampleTexture(${n}, uv);
     }
-  `}let{newShape:s,keptDims:i}=v.squeezeShape(t),o=s;if(o.length<t.length){let h=Ml(e,o),d=["row","col"];return`
-      ${Cl(h)}
+  `}let{newShape:s,keptDims:i}=_.squeezeShape(t),o=s;if(o.length<t.length){let h=zl(e,o),d=["row","col"];return`
+      ${Dl(h)}
       float ${r}(int row, int col) {
-        return ${r}(${Fl(d,i)});
+        return ${r}(${Pl(d,i)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
-        ${Rl(e)}
+        ${Ol(e)}
       }
-    `;let l=a[0],c=a[1],u=Ni(n);return c===1?`
+    `;let l=a[0],c=a[1],u=Ci(n);return c===1?`
     float ${r}(int row, int col) {
       float index = dot(vec3(row, col, ${u}), vec3(${t[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
@@ -603,10 +603,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${n}, uv);
   }
-`}function dL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=Ml(e,h),f=["b","row","col"];return`
-        ${f3(p)}
+`}function DL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(t[0]===1){let h=t.slice(1),d=[1,2],p=zl(e,h),f=["b","row","col"];return`
+        ${R3(p)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${Fl(f,d)});
+          return ${r}(${Pl(f,d)});
         }
       `}let i=s[0],o=s[1],l=Math.ceil(t[2]/2),c=l*Math.ceil(t[1]/2),u=dn();return`
     vec4 ${r}(int b, int row, int col) {
@@ -614,16 +614,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${i}, ${o}, ${c}, ${l}, b, row, col);
       return ${u.texture2D}(${n}, uv);
     }
-  `}function sL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=v.squeezeShape(t),l=i;if(l.length<t.length){let f=Ml(e,l),m=["row","col","depth"];return`
-        ${Cl(f)}
+  `}function TL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=_.squeezeShape(t),l=i;if(l.length<t.length){let f=zl(e,l),m=["row","col","depth"];return`
+        ${Dl(f)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${Fl(m,o)});
+          return ${r}(${Pl(m,o)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${s}, 1)));
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let c=e.shapeInfo.texShape,u=c[0],h=c[1],d=e.shapeInfo.flatOffset;if(h===a&&d==null)return`
         float ${r}(int row, int col, int depth) {
@@ -640,14 +640,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${h}.0, ${u}.0);
       return sampleTexture(${n}, uv);
     }
-  `;let p=Ni(n);return`
+  `;let p=Ci(n);return`
       float ${r}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
         int index = row * ${a} + col * ${s} + depth + ${p};
         vec2 uv = uvFromFlat(${u}, ${h}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function pL(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=dn();return`
+  `}function OL(e){let t=e.shapeInfo.logicalShape,n=t.length,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],l=i[1],c=Math.ceil(t[n-1]/2),u=c*Math.ceil(t[n-2]/2),h="int b, int row, int col",d=`b * ${u} + (row / 2) * ${c} + (col / 2)`;for(let f=2;f<n-1;f++)h=`int b${f}, `+h,u*=t[n-f-1],d=`b${f} * ${u} + `+d;let p=dn();return`
     vec4 ${a}(${h}) {
       int index = ${d};
       int texR = index / ${l};
@@ -655,16 +655,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${o});
       return ${p.texture2D}(${r}, uv);
     }
-  `}function iL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=v.squeezeShape(t);if(o.length<t.length){let f=Ml(e,o),m=["row","col","depth","depth2"];return`
-      ${Cl(f)}
+  `}function EL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[3],s=t[2]*a,i=t[1]*s,{newShape:o,keptDims:l}=_.squeezeShape(t);if(o.length<t.length){let f=zl(e,o),m=["row","col","depth","depth2"];return`
+      ${Dl(f)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${Fl(m,l)});
+        return ${r}(${Pl(m,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${i}, ${s}, ${a}, 1)));
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let c=e.shapeInfo.flatOffset,u=e.shapeInfo.texShape,h=u[0],d=u[1];if(d===i&&c==null)return`
       float ${r}(int row, int col, int depth, int depth2) {
@@ -685,7 +685,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${d}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let p=Ni(n);return`
+    `;let p=Ci(n);return`
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${i} + col * ${s} +
@@ -693,10 +693,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${d}, index + ${p});
       return sampleTexture(${n}, uv);
     }
-  `}function oL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=Ml(e,l),A=["row","col","depth","depth2","depth3"];return`
-      ${Cl(m)}
+  `}function CL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:c}=_.squeezeShape(t);if(l.length<t.length){let m=zl(e,l),y=["row","col","depth","depth2","depth3"];return`
+      ${Dl(m)}
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
-        return ${r}(${Fl(A,c)});
+        return ${r}(${Pl(y,c)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
@@ -704,7 +704,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${s}, ${a})) +
           depth3;
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let u=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,d=h[0],p=h[1];if(p===o&&u==null)return`
       float ${r}(int row, int col, int depth, int depth2, int depth3) {
@@ -726,7 +726,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${p}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let f=Ni(n);return`
+    `;let f=Ci(n);return`
     float ${r}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${o} + col * ${i} + depth * ${s} +
@@ -734,11 +734,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${p}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function lL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=v.squeezeShape(t);if(a.length<t.length){let A=Ml(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
-      ${Cl(A)}
+  `}function RL(e){let t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=_.squeezeShape(t);if(a.length<t.length){let y=zl(e,a),A=["row","col","depth","depth2","depth3","depth4"];return`
+      ${Dl(y)}
       float ${r}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${r}(${Fl(y,s)});
+        return ${r}(${Pl(A,s)});
       }
     `}let i=t[5],o=t[4]*i,l=t[3]*o,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth,
@@ -749,7 +749,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${Rl(e)}
+        ${Ol(e)}
       }
     `;let h=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],f=d[1];if(f===u&&h==null)return`
       float ${r}(int row, int col, int depth,
@@ -775,7 +775,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${p}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let m=Ni(n);return`
+    `;let m=Ci(n);return`
     float ${r}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -784,42 +784,42 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${f}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function Rl(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function Ol(e){let t=e.name,n=_.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
       }
     }
-  `}function fL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=p3(e.shapeInfo.logicalShape,t.logicalShape),l=ot(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(A=>`coords.${h[A+c]} = 0;`).join(`
-`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((A,y)=>`coords.${h[y+c]}`).join(", ");let p="return outputValue;",f=v.sizeFromShape(e.shapeInfo.logicalShape)===1,m=v.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
+  `}function zL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=C3(e.shapeInfo.logicalShape,t.logicalShape),l=lt(i),c=i-s,u,h=["x","y","z","w","u","v"];s===0?u="":i<2&&o.length>=1?u="coords = 0;":u=o.map(y=>`coords.${h[y+c]} = 0;`).join(`
+`);let d="";i<2&&s>0?d="coords":d=e.shapeInfo.logicalShape.map((y,A)=>`coords.${h[A+c]}`).join(", ");let p="return outputValue;",f=_.sizeFromShape(e.shapeInfo.logicalShape)===1,m=_.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)p=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(f&&!m)i===1?p=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
       `:p=`
         return vec4(outputValue.x);
-      `;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?p="return vec4(outputValue.x);":o.indexOf(A)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
+      `;else if(o.length){let y=s-2,A=s-1;o.indexOf(y)>-1&&o.indexOf(A)>-1?p="return vec4(outputValue.x);":o.indexOf(y)>-1?p="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(A)>-1&&(p="return vec4(outputValue.xx, outputValue.zz);")}return`
     vec4 ${a}() {
       ${l} coords = getOutputCoords();
       ${u}
       vec4 outputValue = get${r}(${d});
       ${p}
     }
-  `}function mL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(i,s))return`
+  `}function PL(e,t){let n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&_.arraysEqual(i,s))return`
       float ${a}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let c=ot(l),u=p3(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(m=>`coords.${p[m+h]} = 0;`).join(`
-`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,A)=>`coords.${p[A+h]}`).join(", "),`
+    `;let c=lt(l),u=C3(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,d,p=["x","y","z","w","u","v"];o===0?d="":l<2&&u.length>=1?d="coords = 0;":d=u.map(m=>`coords.${p[m+h]} = 0;`).join(`
+`);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,y)=>`coords.${p[y+h]}`).join(", "),`
     float ${a}() {
       ${c} coords = getOutputCoords();
       ${d}
       return get${r}(${f});
     }
-  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ml(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Fl(e,t){return t.map(n=>e[n]).join(", ")}function EL(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=tL(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function A3(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!v.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!v.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function CL(e,t,n,r,a){A3(t.inShapeInfos,n),A3([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(v.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function RL(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:ML,bincountImpl:y3,bincountReduceImpl:FL,ceilImpl:DL,concatImpl:$L,expImpl:OL,expm1Impl:zL,floorImpl:PL,gatherV2Impl:LL,greaterImpl:WL,lessImpl:BL,linSpaceImpl:VL,logImpl:jL,maxImpl:UL,maximumImpl:HL,minimumImpl:GL,multiplyImpl:qL,negImpl:XL,prodImpl:KL,rangeImpl:ZL,rsqrtImpl:YL,simpleAbsImpl:g3,sliceImpl:JL,stridedSliceImpl:QL,subImpl:eW,tileImpl:tW,topKImpl:nW,transposeImpl:fA,uniqueImpl:rW}=Bm;function x3(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function pn(e,t){return t===1?[e]:x3(e,t)}function aW(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var lW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
+  `}function lt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function zl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Pl(e,t){return t.map(n=>e[n]).join(", ")}function JL(e,t,n,r){let a=t.userCode,s=n.map((p,f)=>{let m={logicalShape:p.shape,texShape:p.isUniform?null:p.texData.texShape,isUniform:p.isUniform,isPacked:p.isUniform?!1:p.texData.isPacked,flatOffset:null};return p.texData!=null&&p.texData.slice!=null&&p.texData.slice.flatOffset>0&&(m.flatOffset=p.texData.slice.flatOffset),{name:t.variableNames[f],shapeInfo:m}}),i=s.map(p=>p.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=kL(s,o,a,t.packedInputs),c=e.createProgram(l),u=null,h=e.getUniformLocation(c,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let d={};for(let p=0;p<t.variableNames.length;p++){let f=t.variableNames[p],m=!1;d[f]=e.getUniformLocation(c,f,m),d[`offset${f}`]=e.getUniformLocation(c,`offset${f}`,m)}return{program:t,source:l,webGLProgram:c,uniformLocations:d,inShapeInfos:i,outShapeInfo:o,infLoc:u,nanLoc:h}}function F3(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,r)=>{let a=n.logicalShape,s=t[r],i=s.shape;if(!_.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,l=s.isUniform?null:s.texData.texShape;if(!_.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function QL(e,t,n,r,a){F3(t.inShapeInfos,n),F3([t.outShapeInfo],[r]);let s=r.texData.texture,i=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,l)=>{let c=t.program.variableNames[l],u=t.uniformLocations[c],h=t.uniformLocations[`offset${c}`];if(u!=null){if(o.isUniform){if(_.sizeFromShape(o.shape)<2)e.gl.uniform1f(u,o.uniformValues[0]);else{let d=o.uniformValues;d instanceof Float32Array||(d=new Float32Array(d)),e.gl.uniform1fv(u,d)}return}o.texData.slice!=null&&h!=null&&e.gl.uniform1i(h,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,u,l)}}),a!=null&&a(e,t.webGLProgram),e.executeProgram()}function eW(e,t,n){let r="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,l=i.isUniform?"uniform":i.texData.texShape;r+=`${i.shape}_${l}_${o}`});let a=e.userCode,s=e.constructor.name;return s+="_"+r+"_"+a,s}var{addImpl:tW,bincountImpl:$3,bincountReduceImpl:nW,ceilImpl:rW,concatImpl:aW,expImpl:sW,expm1Impl:iW,floorImpl:oW,gatherV2Impl:lW,greaterImpl:uW,lessImpl:cW,linSpaceImpl:hW,logImpl:dW,maxImpl:pW,maximumImpl:fW,minimumImpl:mW,multiplyImpl:yW,negImpl:AW,prodImpl:gW,rangeImpl:xW,rsqrtImpl:wW,simpleAbsImpl:D3,sliceImpl:bW,stridedSliceImpl:_W,subImpl:vW,tileImpl:kW,topKImpl:IW,transposeImpl:Sy,uniqueImpl:SW}=ey;function O3(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function pn(e,t){return t===1?[e]:O3(e,t)}function NW(e,t){if(e===1)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var RW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let n=pn("rc",t),r=ot(t),a=sW(t,e,n),s=iW(t,e[e.length-1],e[e.length-2],n),i=oW(e,n);this.userCode=`
+      `;else{let n=pn("rc",t),r=lt(t),a=TW(t,e,n),s=EW(t,e[e.length-1],e[e.length-2],n),i=CW(e,n);this.userCode=`
         void main() {
           ${r} rc = getOutputCoords();
 
@@ -831,7 +831,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function uW(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function sW(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function iW(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
+      `}}};function MW(e,t){let n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${r===0?"r":"rp1"}, ${a===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function TW(e,t,n){if(e===1)return`rc > ${t[0]}`;let r="";for(let a=e-2;a<e;a++)r+=`${n[a]} >= ${t[a]}`,a<e-1&&(r+="||");return r}function EW(e,t,n,r){if(e===1)return"";let a=r.slice(-2);return`
     int r = ${a[0]};
     int c = ${a[1]};
     int rp1 = r + 1;
@@ -839,12 +839,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
     bool cEdge = cp1 >= ${t};
     bool rEdge = rp1 >= ${n};
-  `}function oW(e,t){let n=e.length,r=uW(n,t);return n===1?`getA(rc),
+  `}function CW(e,t){let n=e.length,r=MW(n,t);return n===1?`getA(rc),
             rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
             0, 0`:`getA(${r[0]}),
           cEdge ? 0. : getA(${r[1]}),
           rEdge ? 0. : getA(${r[2]}),
-          rEdge || cEdge ? 0. : getA(${r[3]})`}var w3=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
+          rEdge || cEdge ? 0. : getA(${r[3]})`}var z3=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let r=0;r<4;r++){let a="thisRC = rc;";r%2==1&&(a+="thisRC.z += 1;"),r>1&&(a+="thisRC.y += 1;"),n+=`
         ${a}
         ${r>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -856,8 +856,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${r>0?"}":""}
       `}this.userCode=`
-      ${cW(t)}
-      ${lA(e)}
+      ${FW(t)}
+      ${wy(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -872,12 +872,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function cW(e){return`
+    `}};function FW(e){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${Ii(["r","c","d"],e)}
+      ${Ei(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var hW=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=_3(t,n),a=v3(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=b3(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===tn.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===tn.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===tn.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===tn.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===tn.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=_3(n,r),s=v3(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=b3(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function dW(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function b3(e,t,n,r,a){let s=pW(t,r),i;if(a){let[l,c]=El(e[0],e[1]);i=l*c}else{let[l,c]=mc(e[0],e[1]);i=l*c}let o=dW(n,s);return i*o}function pW(e,t){switch(e){case tn.PACKED_2X2_FLOAT32:return dA(t);case tn.PACKED_2X2_FLOAT16:return pA(t);case tn.UNPACKED_FLOAT32:return uA(t);case tn.UNPACKED_FLOAT16:return cA(t);case tn.PACKED_4X1_UNSIGNED_BYTE:return hA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function fW(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?tn.PACKED_2X2_FLOAT32:tn.UNPACKED_FLOAT32:e?tn.PACKED_2X2_FLOAT16:tn.UNPACKED_FLOAT16}function _3(e,t){if(e===nr.UPLOAD)return tn.PACKED_2X2_FLOAT32;if(e===nr.RENDER||e==null)return fW(t);if(e===nr.DOWNLOAD||e===nr.PIXELS)return tn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function v3(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ha=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var $W=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let r=L3(t,n),a=W3(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=P3(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return r===en.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===en.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===en.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===en.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===en.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(this.freeTextures==null)return;let a=L3(n,r),s=W3(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);let i=P3(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function DW(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function P3(e,t,n,r,a){let s=OW(t,r),i;if(a){let[l,c]=$l(e[0],e[1]);i=l*c}else{let[l,c]=wc(e[0],e[1]);i=l*c}let o=DW(n,s);return i*o}function OW(e,t){switch(e){case en.PACKED_2X2_FLOAT32:return ky(t);case en.PACKED_2X2_FLOAT16:return Iy(t);case en.UNPACKED_FLOAT32:return by(t);case en.UNPACKED_FLOAT16:return _y(t);case en.PACKED_4X1_UNSIGNED_BYTE:return vy(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function zW(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?en.PACKED_2X2_FLOAT32:en.UNPACKED_FLOAT32:e?en.PACKED_2X2_FLOAT16:en.UNPACKED_FLOAT16}function L3(e,t){if(e===ar.UPLOAD)return en.PACKED_2X2_FLOAT32;if(e===ar.RENDER||e==null)return zW(t);if(e===ar.DOWNLOAD||e===ar.PIXELS)return en.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function W3(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ga=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Nr="if (isnan(x)) return x;",mW="return x;",k3="return abs(x);",AW="return (x >= 0.0) ? x : (exp(x) - 1.0);",yW=Nr+`
+    `}},Sr="if (isnan(x)) return x;",PW="return x;",B3="return abs(x);",LW="return (x >= 0.0) ? x : (exp(x) - 1.0);",WW=Sr+`
   return (x < 0.0) ? 0.0 : x;
-`,gW=Nr+`
+`,BW=Sr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,yp="return x;",xW="return x;",wW=`
+`,bp="return x;",VW="return x;",jW=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,bW=`
+`,UW=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,_W=`
+`,HW=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -921,7 +921,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Dl=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+`,Ll=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -932,17 +932,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},vW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=pn("rc",t),r=ot(t),a=aW(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},GW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=pn("rc",t),r=lt(t),a=NW(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${r} rc = getOutputCoords();
         vec4 packedInput = getA(${a});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},kW=Hr.whereImpl,IW=1e-7,NW=1e-4,mA={};function SW(e){return e in mA||(mA[e]={}),mA[e]}var TW=128,EW=600;function CW(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*EW/1024/1024}var $l=class extends fu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=qr(J().getNumber("WEBGL_VERSION"));this.binaryCache=SW(J().getNumber("WEBGL_VERSION")),this.gpgpu=new Ap(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new hW(this.gpgpu),this.numMBBeforeWarning=CW(),this.texData=new Nh(this,Lr())}nextDataId(){return $l.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:nr.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:nr.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Dl(i,yp):h=new Ha(i,yp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=R.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Dl(r,yp):p=new Ha(r,yp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...Ac(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=R.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=v.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Lr().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>v.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!I_(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=v.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...Ac(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?hp(t):t,o=s?new jP(i):new VP(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=v.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=v.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=v.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return J().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Lr().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=TW){let n=this.getCPUBackend();return!J().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&n==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),n!=null&&e.every(r=>this.texData.get(r.dataId).texture==null&&v.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){R.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return kW(e.shape,t)}packedUnaryOp(e,t,n){let r=new Dl(e.shape,t),a=this.compileAndRun(r,[e],n);return Lr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=g3(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,k3,e.dtype);let t=new Ha(e.shape,k3),n=this.compileAndRun(t,[e]);return Lr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let a=n.map(s=>v.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return Lr().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new vW(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new lW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[_i(e.shape),...vi(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[_i(t),...vi(t)],s=new w3(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=hp(r),i;n?i=new BP(s):i=new WP(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===fc.DENSE){let m=Ac(e.outputShape);i.texShape=m.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(s.shape)===0)return i.values=v.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(m.dataId);if(A.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=m.shape)}else if(!!A.isPacked!=!!e.packedInputs)m=A.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),A=this.texData.get(m.dataId);else if(A.isPacked&&!pc(A.shape,m.shape)){let y=m,g=m.shape;m.shape=A.shape,m=this.packedReshape(m,g),o.push(m),A=this.texData.get(m.dataId),y.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=RL(e,l,c),h=this.getAndSaveBinary(u,()=>EL(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),CL(this.gpgpu,h,l,c,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(ge(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?IW:NW}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=B_(n,o),t.texShape=u),a!=null){let h=hp(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=El(u[0],u[1]),d=new HP(h,[f,p],m)):d=new UP(h,[f,p],m);let A=this.makeTensorInfo([f,p],r);m?this.texData.get(A.dataId).usage=nr.PIXELS:this.texData.get(A.dataId).usage=nr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),p,f,a);let y=!0,g=this.runWebGLProgram(d,[A],r,null,y),w=this.texData.get(g.dataId);t.texture=w.texture,t.texShape=w.texShape,t.isPacked=w.isPacked,t.usage=w.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=RW(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}};$l.nextDataId=0;function RW(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var I3="3.3.0";function N3(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}Vu.isBrowser()&&cl("webgl",()=>new $l,2);var MW={forceHalfFloat:N3},S3=`
+    `}},qW=Hr.whereImpl,XW=1e-7,KW=1e-4,Ny={};function ZW(e){return e in Ny||(Ny[e]={}),Ny[e]}var YW=128,JW=600;function QW(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*JW/1024/1024}var Wl=class extends wu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=qr(J().getNumber("WEBGL_VERSION"));this.binaryCache=ZW(J().getNumber("WEBGL_VERSION")),this.gpgpu=new wp(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new $W(this.gpgpu),this.numMBBeforeWarning=QW(),this.texData=new Rh(this,ua())}nextDataId(){return Wl.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:ar.UPLOAD,refCount:1}),r}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,r,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),r==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:ar.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Ll(i,bp):h=new Ga(i,bp);let d=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:r}],r),p=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),p}if(n!=null)return this.convertAndCacheOnCPU(e);if(r==="string")return n;let l=this.activeTimers!=null,c;l&&(c=_.now());let u;if(r==="complex64"){let h=this.readSync(a.real.dataId),d=this.readSync(a.imag.dataId);u=C.mergeRealAndImagArrays(h,d)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=_.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let p=this.pendingRead.get(e);return new Promise(f=>p.push(f))}let t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let p;o?p=new Ll(r,bp):p=new Ga(r,bp);let f=this.runWebGLProgram(p,[{dataId:e,shape:r,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let p=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(p.texture,...bc(r))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(s==="complex64"){let p=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=p[0],m=p[1];u=C.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let p=_.sizeFromShape(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,p)}c!=null&&this.disposeIntermediateTensorInfo(c);let h=this.convertAndCacheOnCPU(e,u),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(p=>p(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ua().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(r=>_.decodeString(r))}catch(r){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!V_(n))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=_.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),d=this.texData.get(h.dataId),p=this.gpgpu.downloadMatrixFromPackedTexture(d.texture,...bc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),p}let s=J().getBool("WEBGL_PACK")&&r===!0,i=s?mp(t):t,o=s?new dL(i):new hL(i),l=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],r=!1;this.programTimersStack==null?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();let a=_.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=_.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,r&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=_.sum(o),i.getExtraProfileInfo=()=>o.map((l,c)=>({name:s[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:_.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=_.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=YW){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&_.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return qW(e.shape,t)}packedUnaryOp(e,t,n){let r=new Ll(e.shape,t),a=this.compileAndRun(r,[e],n);return ua().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let r=D3(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,r)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,B3,e.dtype);let t=new Ga(e.shape,B3),n=this.compileAndRun(t,[e]);return ua().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let r;if(t==="string"&&n!=null&&n.length>0&&_.isString(n[0])){let a=n.map(s=>_.encodeString(s));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:r}=this.makeTensorInfo(e,t,n);return ua().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){let t=new GW(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new RW(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Si(e.shape),...Ni(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[Si(t),...Ni(t)],s=new z3(a,n),i=!0,o=this.runWebGLProgram(s,[r],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=mp(r),i;n?i=new cL(s):i=new uL(s);let o=!0,l=this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,o);return{dtype:a,shape:r,dataId:l.dataId}}runWebGLProgram(e,t,n,r,a=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===xc.DENSE){let m=bc(e.outputShape);i.texShape=m.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),_.sizeFromShape(s.shape)===0)return i.values=_.getTypedArrayFromDType(s.dtype,0),s;let o=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(m.dataId);if(y.texture==null){if(!e.packedInputs&&_.sizeFromShape(m.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=m.shape)}else if(!!y.isPacked!=!!e.packedInputs)m=y.isPacked?this.unpackTensor(m):this.packTensor(m),o.push(m),y=this.texData.get(m.dataId);else if(y.isPacked&&!gc(y.shape,m.shape)){let A=m,g=m.shape;m.shape=y.shape,m=this.packedReshape(m,g),o.push(m),y=this.texData.get(m.dataId),A.shape=g}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:y,isUniform:!1}});this.uploadToGPU(s.dataId);let c={shape:s.shape,texData:i,isUniform:!1},u=eW(e,l,c),h=this.getAndSaveBinary(u,()=>JL(this.gpgpu,e,l,c)),d=this.activeTimers!=null,p;d&&(p=this.startTimer()),QL(this.gpgpu,h,l,c,r),o.forEach(m=>this.disposeIntermediateTensorInfo(m)),d&&(p=this.endTimer(p),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(p)}));let f=J().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=_.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&a===!1){let m=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),m}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=z(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(xe(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?XW:KW}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,c;l&&(c=_.now());let u=t.texShape;if(u==null&&(u=a3(n,o),t.texShape=u),a!=null){let h=mp(n),d,p=u[1],f=u[0],m=a instanceof Uint8Array;o?([p,f]=$l(u[0],u[1]),d=new fL(h,[f,p],m)):d=new pL(h,[f,p],m);let y=this.makeTensorInfo([f,p],r);m?this.texData.get(y.dataId).usage=ar.PIXELS:this.texData.get(y.dataId).usage=ar.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),p,f,a);let A=!0,g=this.runWebGLProgram(d,[y],r,null,A),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(g.dataId),t.values=null,l&&(this.uploadWaitMs+=_.now()-c)}else{let h=this.acquireTexture(u,i,r,o);t.texture=h}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),t!=null&&(n.values=eB(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*_.bytesPerElement(t)}};Wl.nextDataId=0;function eB(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}else throw new Error(`Unknown dtype ${t}`)}var V3="3.4.0";function j3(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}qu.isBrowser()&&fl("webgl",()=>new Wl,2);var tB={forceHalfFloat:j3},U3=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,Ol=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
+`,Bl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -952,17 +952,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},gp=`
+    `}},_p=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`,gc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=R.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||v.sizeFromShape(this.outputShape)===1)s=`
+`,vc=class{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let a=this.outputShape.length,s="";if(r)if(a===0||_.sizeFromShape(this.outputShape)===1)s=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
         `;else if(s=`
-          ${ot(a)} coords = getOutputCoords();
+          ${lt(a)} coords = getOutputCoords();
         `,a===1)s+=`
             result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
             result.z = 0.;
@@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Wn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var FW={kernelName:Ss,backendName:"webgl",kernelFunc:Wn};function Ga(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Wn({inputs:{x:r},backend:n}),l=Wn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var DW={kernelName:$h,backendName:"webgl",kernelFunc:Ga},T3="return (a < 0.) ? b * a : a;",E3=`
+    `}};function Vn(e){let{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var nB={kernelName:Es,backendName:"webgl",kernelFunc:Vn};function qa(e){let{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=Vn({inputs:{x:r},backend:n}),l=Vn({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}var rB={kernelName:Ph,backendName:"webgl",kernelFunc:qa},H3="return (a < 0.) ? b * a : a;",G3=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function $W(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",v.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new gc(E3,a.shape,i.shape):new Ol(T3,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var OW={kernelName:Ts,backendName:"webgl",kernelFunc:$W},C3="return (a < 0.) ? b * a : a;",R3=`
+`;function aB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",_.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(G3,a.shape,i.shape):new Bl(H3,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}var sB={kernelName:Cs,backendName:"webgl",kernelFunc:aB},q3="return (a < 0.) ? b * a : a;",X3=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function zW(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new gc(R3,r.shape,a.shape):new Ol(C3,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var PW={kernelName:Ws,backendName:"webgl",kernelFunc:zW},M3="if (isnan(x)) return x;",LW=`
+`;function iB(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(X3,r.shape,a.shape):new Bl(q3,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}var oB={kernelName:js,backendName:"webgl",kernelFunc:iB},K3="if (isnan(x)) return x;",lB=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,WW=`
+`,uB=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Dl(i.shape,t):u=new Ha(i.shape,e),o.runWebGLProgram(u,[i],l)}}function nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(w=>{let[b,_]=w,x={dataId:b.dataId,dtype:b.dtype,shape:l.shape},N={dataId:_.dataId,dtype:_.dtype,shape:c.shape},T=new Ol(e,l.shape,c.shape);return u.runWebGLProgram(T,[x,N],cr(b.dtype,_.dtype))}),g=Ga({inputs:{real:A,imag:y},backend:u});return u.disposeIntermediateTensorInfo(A),u.disposeIntermediateTensorInfo(y),g}let h=s||cr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[A,y]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(y,h),w=u.texData.get(g.dataId);return w.values=A,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new gc(t,l.shape,c.shape,n):p=new Ol(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function xp(e,t=!1){if(e==="linear")return t?xW:mW;if(e==="relu")return t?bW:yW;if(e==="elu")return t?wW:AW;if(e==="relu6")return t?_W:gW;if(e==="prelu")return t?R3:C3;if(e==="leakyrelu")return t?E3:T3;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var F3=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",A="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Xe({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let h=o.texData.get(i.dataId),d=n(h.values,l);return o.makeTensorInfo(i.shape,l,d)}let c=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new Ll(i.shape,t):u=new Ga(i.shape,e),o.runWebGLProgram(u,[i],l)}}function tn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:c}=i,u=o;if(r&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[y,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[v,w]=x,b={dataId:v.dataId,dtype:v.dtype,shape:l.shape},k={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new Bl(e,l.shape,c.shape);return u.runWebGLProgram(N,[b,k],dr(v.dtype,w.dtype))}),g=qa({inputs:{real:y,imag:A},backend:u});return u.disposeIntermediateTensorInfo(y),u.disposeIntermediateTensorInfo(A),g}let h=s||dr(l.dtype,c.dtype);if(u.shouldExecuteOnCPU([l,c])&&a!=null){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[y,A]=a(l.shape,c.shape,f.values,m.values,h),g=u.makeTensorInfo(A,h),x=u.texData.get(g.dataId);return x.values=y,g}let d=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,p;return d?p=new vc(t,l.shape,c.shape,n):p=new Bl(e,l.shape,c.shape),u.runWebGLProgram(p,[l,c],h)}}function vp(e,t=!1){if(e==="linear")return t?VW:PW;if(e==="relu")return t?UW:WW;if(e==="elu")return t?jW:LW;if(e==="relu6")return t?HW:BW;if(e==="prelu")return t?X3:q3;if(e==="leakyrelu")return t?G3:H3;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Z3=class{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=r?e[1]:e[2],u=Math.ceil(c/2),h=r?"i * 2, rc.y":"rc.y, i * 2",d=a?"rc.z, i * 2":"i * 2, rc.z",p=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",y="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1011,7 +1011,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           ${i}
         }`:m=`vec4 activation(vec4 x) {
           ${i}
-        }`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",w="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(w=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
+        }`,y="result = activation(result);");let A=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
       ${m}
 
       const float sharedDimension = ${u}.0;
@@ -1020,7 +1020,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         vec4 result = vec4(0);
         for (int i = 0; i < ${u}; i++) {
           int batchA = ${g};
-          int batchB = ${w};
+          int batchB = ${x};
           vec4 a = getMatrixA(batchA, ${h});
           vec4 b = getMatrixB(batchB, ${d});
 
@@ -1036,13 +1036,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ivec3 rc = getOutputCoords();
         vec4 result = dot2x2ARowBCol(rc);
 
-        ${y}
-
         ${A}
 
+        ${y}
+
         setOutput(result);
       }
-    `}},D3={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},$3=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=R.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},Y3={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},J3=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},O3="return a * b;";function z3(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=R.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new $3(D3.REAL,r.shape,a.shape),u=new $3(D3.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Ga({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=qL(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new gc(O3,r.shape,a.shape):i=new Ol(O3,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var BW={kernelName:Os,backendName:"webgl",kernelFunc:z3};function VW(e,t,n){let r=[_i(e.shape),...vi(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[_i(t),...vi(t)],i=new w3(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function xe(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=v.sizeFromShape(a.shape),l=v.inferFromImplicitShape(s,o),c=v.sizeFromShape(l);v.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!pc(a.shape,l)&&!(u.texture!==null&&pc(u.shape,l))?VW(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var jW={kernelName:Bo,backendName:"webgl",kernelFunc:xe},P3=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
+    `}},Q3="return a * b;";function Ty(e){let{inputs:t,backend:n}=e,{a:r,b:a}=t,s=C.upcastType(r.dtype,a.dtype);if(r.dtype==="complex64"){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),c=new J3(Y3.REAL,r.shape,a.shape),u=new J3(Y3.IMAG,r.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:r.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}if(n.shouldExecuteOnCPU([r,a])){let o=n.texData.get(r.dataId),l=n.texData.get(a.dataId),[c,u]=yW(r.shape,a.shape,o.values,l.values,s),h=n.makeTensorInfo(u,s),d=n.texData.get(h.dataId);return d.values=c,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new vc(Q3,r.shape,a.shape):i=new Bl(Q3,r.shape,a.shape),n.runWebGLProgram(i,[r,a],s)}var cB={kernelName:Ls,backendName:"webgl",kernelFunc:Ty};function hB(e,t,n){let r=[Si(e.shape),...Ni(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[Si(t),...Ni(t)],i=new z3(s,r),o=!0,l=n.runWebGLProgram(i,[a],e.dtype,null,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function Ae(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=n,o=_.sizeFromShape(a.shape),l=_.inferFromImplicitShape(s,o),c=_.sizeFromShape(l);_.assert(o===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let u=i.texData.get(a.dataId);return u.isPacked&&!gc(a.shape,l)&&!(u.texture!==null&&gc(u.shape,l))?hB(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var dB={kernelName:Go,backendName:"webgl",kernelFunc:Ae},e7=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i=Math.floor(n/4)*4,o=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${_.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";a%n>0&&(c=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},UW=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
+    `}},pB=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,h=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1193,12 +1193,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function HW(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=R.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Si(e,t,n,r){let a=HW(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new P3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new P3({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new UW({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var qW=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=ot(this.rank),a=GW(t);this.userCode=`
+    `}};function fB(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],r=C.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}function Ri(e,t,n,r){let a=fB(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:c}=a[i],u,h;n==="mean"?u=i===0?new e7({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},o):new e7({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c}):u=new pB({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:c},n),h=s,s=r.runWebGLProgram(u,[s],t),h.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(h)}return s}var yB=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let r=lt(this.rank),a=mB(t);this.userCode=`
     void main() {
       ${r} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function GW(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var XW=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=ot(this.rank),a=x3("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function mB(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}var AB=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let r=lt(this.rank),a=O3("rc",this.rank),s=new Array(this.rank);for(let c=0;c<t.length;c++)s[t[c]]=a[c];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${r} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1215,14 +1215,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function wp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XW(e.shape,t):new qW(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function KW(e,t,n,r){let a=t,s=e.shape.length,i=v.parseAxisParam(a,e.shape),o=i,l=R.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=wp(e,l,r),o=R.getInnerMostAxes(o.length,s)),R.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=R.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=R.expandShapeToKeepDim(h,i));let f=v.sizeFromShape(d),m=v.sizeFromShape(e.shape)/f,A=xe({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),y=dd(e.dtype),g=Si(A,y,"sum",r),w=xe({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(A),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),w}function AA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return KW(a,s,i,n)}var ZW={kernelName:Zs,backendName:"webgl",kernelFunc:AA};function En(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=fA(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=wp(a,s,i);return c}var YW={kernelName:ti,backendName:"webgl",kernelFunc:En},L3=1e3;function bp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=v.sizeFromShape(m),g=v.sizeFromShape(A),w=y===g||y===1||g===1;v.assert(c>=2&&u>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${A}).`);let b=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);v.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let _=n?[y,h,p]:[y,p,h],x=r?[g,f,d]:[g,d,f],N=xe({inputs:{x:e},backend:a,attrs:{shape:_}}),T=xe({inputs:{x:t},backend:a,attrs:{shape:x}}),E=[N,T],F=Math.max(y,g),$=n?N.shape[1]:N.shape[2],L=s!=null,V=i!=null,j=l==="leakyrelu",U=l!=null?xp(l,!0):null,X=L||V||j||U!=null,G;if((p===1||f===1)&&$>L3&&X===!1){let Y=N,ae=T;n&&(Y=En({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(ae=En({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let te=f!==1,ie=f===1,Q=Y;te&&(Q=xe({inputs:{x:Y},backend:a,attrs:{shape:[F,$,1]}}),E.push(Q));let he=f===1?2:1,oe=ae;ie&&(oe=xe({inputs:{x:ae},backend:a,attrs:{shape:[F,1,$]}}),E.push(oe));let fe=z3({inputs:{a:Q,b:oe},backend:a});G=AA({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),E.push(fe)}else{let Y=cr(e.dtype,t.dtype),ae=new F3(_,x,[F,p,f],n,r,L,U,V,j),te=[N,T];if(s!=null&&te.push(s),V&&te.push(i),j){let ie=a.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=xe({inputs:{x:G},backend:a,attrs:{shape:b}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function JW(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return bp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var QW={kernelName:ni,backendName:"webgl",kernelFunc:JW},W3="return abs(x);";function eB(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=g3(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Dl(r.shape,W3):a=new Ha(r.shape,W3),n.runWebGLProgram(a,[r],r.dtype)}var tB={kernelName:no,backendName:"webgl",kernelFunc:eB},nB=Nr+`
+    `}};function kp(e,t,n){let r=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new AB(e.shape,t):new yB(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function gB(e,t,n,r){let a=t,s=e.shape.length,i=_.parseAxisParam(a,e.shape),o=i,l=C.getAxesPermutation(o,s),c=l!=null,u=e;c&&(u=kp(e,l,r),o=C.getInnerMostAxes(o.length,s)),C.assertAxesAreInnerMostDims("sum",o,s);let[h,d]=C.computeOutAndReduceShapes(u.shape,o),p=h;n&&(p=C.expandShapeToKeepDim(h,i));let f=_.sizeFromShape(d),m=_.sizeFromShape(e.shape)/f,y=Ae({inputs:{x:u},attrs:{shape:[m,f]},backend:r}),A=yd(e.dtype),g=Ri(y,A,"sum",r),x=Ae({inputs:{x:g},attrs:{shape:p},backend:r});return r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(g),c&&r.disposeIntermediateTensorInfo(u),x}function Ip(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r;return gB(a,s,i,n)}var xB={kernelName:Qs,backendName:"webgl",kernelFunc:Ip};function fn(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,i=n,o=a.shape.length,l=new Array(o);for(let u=0;u<l.length;u++)l[u]=a.shape[s[u]];let c;if(i.shouldExecuteOnCPU([a])){let u=i.texData.get(a.dataId).values,h=Sy(u,a.shape,a.dtype,s,l);c=i.makeTensorInfo(l,a.dtype);let d=i.texData.get(c.dataId);d.values=h}else c=kp(a,s,i);return c}var wB={kernelName:si,backendName:"webgl",kernelFunc:fn},t7=1e3;function Sp({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],d=r?t.shape[u-1]:t.shape[u-2],p=n?e.shape[c-1]:e.shape[c-2],f=r?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),y=t.shape.slice(0,-2),A=_.sizeFromShape(m),g=_.sizeFromShape(y),x=A===g||A===1||g===1;_.assert(c>=2&&u>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${y}).`);let v=(A>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([p,f]);_.assert(h===d,()=>`Error in matMul: inner shapes (${h}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);let w=n?[A,h,p]:[A,p,h],b=r?[g,f,d]:[g,d,f],k=Ae({inputs:{x:e},backend:a,attrs:{shape:w}}),N=Ae({inputs:{x:t},backend:a,attrs:{shape:b}}),E=[k,N],F=Math.max(A,g),O=n?k.shape[1]:k.shape[2],L=s!=null,V=i!=null,j=l==="leakyrelu",U=l!=null?vp(l,!0):null,X=L||V||j||U!=null,G;if((p===1||f===1)&&O>t7&&X===!1){let Y=k,ae=N;n&&(Y=fn({inputs:{x:k},backend:a,attrs:{perm:[0,2,1]}}),E.push(Y)),r&&(ae=fn({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),E.push(ae));let te=f!==1,ie=f===1,Q=Y;te&&(Q=Ae({inputs:{x:Y},backend:a,attrs:{shape:[F,O,1]}}),E.push(Q));let he=f===1?2:1,oe=ae;ie&&(oe=Ae({inputs:{x:ae},backend:a,attrs:{shape:[F,1,O]}}),E.push(oe));let fe=Ty({inputs:{a:Q,b:oe},backend:a});G=Ip({inputs:{x:fe},backend:a,attrs:{axis:he,keepDims:!0}}),E.push(fe)}else{let Y=dr(e.dtype,t.dtype),ae=new Z3(w,b,[F,p,f],n,r,L,U,V,j),te=[k,N];if(s!=null&&te.push(s),V&&te.push(i),j){let ie=a.makeTensorInfo([],"float32",_.createScalarValue(o,"float32"));te.push(ie),E.push(ie)}G=a.runWebGLProgram(ae,te,Y)}let ee=Ae({inputs:{x:G},backend:a,attrs:{shape:v}});E.push(G);for(let Y of E)a.disposeIntermediateTensorInfo(Y);return ee}function bB(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:h}=r;return Sp({a,b:s,transposeA:l,transposeB:c,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:u})}var _B={kernelName:ii,backendName:"webgl",kernelFunc:bB},n7="return abs(x);";function vB(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&r.dtype!=="complex64"){let s=n.texData.get(r.dataId),i=D3(s.values);return n.makeTensorInfo(r.shape,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,n7):a=new Ga(r.shape,n7),n.runWebGLProgram(a,[r],r.dtype)}var kB={kernelName:so,backendName:"webgl",kernelFunc:vB},IB=Sr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,rB=Xe({opSnippet:nB}),aB={kernelName:ro,backendName:"webgl",kernelFunc:rB},sB=Nr+`
+`,SB=Xe({opSnippet:IB}),NB={kernelName:io,backendName:"webgl",kernelFunc:SB},TB=Sr+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,iB=Xe({opSnippet:sB}),oB={kernelName:ao,backendName:"webgl",kernelFunc:iB},B3="return a + b;",lB=nn({opSnippet:B3,packedOpSnippet:B3,supportsComplex:!0,cpuKernelImpl:ML}),uB={kernelName:Ea,backendName:"webgl",kernelFunc:lB},cB=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,EB=Xe({opSnippet:TB}),CB={kernelName:oo,backendName:"webgl",kernelFunc:EB},r7="return a + b;",RB=tn({opSnippet:r7,packedOpSnippet:r7,supportsComplex:!0,cpuKernelImpl:tW}),MB={kernelName:Ca,backendName:"webgl",kernelFunc:RB},FB=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`float v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1230,7 +1230,7 @@ return log(x + sqrt(x * x - 1.0));`,iB=Xe({opSnippet:sB}),oB={kernelName:ao,back
         float result = ${r};
         setOutput(result);
       }
-    `}},hB=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+    `}},$B=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let n=[];this.variableNames.forEach(a=>{n.push(`vec4 v${a} = get${a}AtOutCoords();`)});let r=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1238,7 +1238,7 @@ return log(x + sqrt(x * x - 1.0));`,iB=Xe({opSnippet:sB}),oB={kernelName:ao,back
         vec4 result = ${r};
         setOutput(result);
       }
-    `}};function _p(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Wn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=_p({inputs:r.slice(0,o),backend:n}),c=_p({inputs:r.slice(o),backend:n});return _p({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>cr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new hB(r[0].shape,s):new cB(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var dB={kernelName:cs,backendName:"webgl",kernelFunc:_p};function pB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("all",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Si(m,m.dtype,"all",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var fB={kernelName:Ch,backendName:"webgl",kernelFunc:pB};function mB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,o)),R.assertAxesAreInnerMostDims("any",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Si(m,m.dtype,"any",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var AB={kernelName:Rh,backendName:"webgl",kernelFunc:mB},yB=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function Np(e){let{inputs:t,backend:n}=e,r=t;if(r.length===1)return Vn({inputs:{x:r[0]},backend:n});if(r.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(r.length/2),l=Np({inputs:r.slice(0,o),backend:n}),c=Np({inputs:r.slice(o),backend:n});return Np({inputs:[l,c],backend:n})}let a=r.map(o=>o.dtype).reduce((o,l)=>dr(o,l)),s=r.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new $B(r[0].shape,s):new FB(r[0].shape,s);return n.runWebGLProgram(i,r,a)}var DB={kernelName:ds,backendName:"webgl",kernelFunc:Np};function OB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("all",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"all",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var zB={kernelName:lo,backendName:"webgl",kernelFunc:OB};function PB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,o)),C.assertAxesAreInnerMostDims("any",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"any",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var LB={kernelName:uo,backendName:"webgl",kernelFunc:PB},WB=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1258,14 +1258,14 @@ return log(x + sqrt(x * x - 1.0));`,iB=Xe({opSnippet:sB}),oB={kernelName:ao,back
         }
         setOutput(float(bestIndex));
       }
-    `}},gB=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=ot(o),c=pn("coords",o),u,h;if(s===1){h=o+1;let N=ot(h);u=`
-        ${N} sourceLocR = ${N}(${c.join()}, 0);
+    `}},BB=class{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,_.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=lt(o),c=pn("coords",o),u,h;if(s===1){h=o+1;let k=lt(h);u=`
+        ${k} sourceLocR = ${k}(${c.join()}, 0);
         ++${c[o-1]};
-        ${N} sourceLocG = ${N}(${c.join()}, 0);
+        ${k} sourceLocG = ${k}(${c.join()}, 0);
         ++${c[o-2]};
-        ${N} sourceLocA = ${N}(${c.join()}, 0);
+        ${k} sourceLocA = ${k}(${c.join()}, 0);
         --${c[o-1]};
-        ${N} sourceLocB = ${N}(${c.join()}, 0);
+        ${k} sourceLocB = ${k}(${c.join()}, 0);
         --${c[o-2]};`}else h=o,u=`
         ${l} sourceLocR = coords;
         ++${c[o-1]};
@@ -1274,15 +1274,15 @@ return log(x + sqrt(x * x - 1.0));`,iB=Xe({opSnippet:sB}),oB={kernelName:ao,back
         ${l} sourceLocA = coords;
         --${c[o-1]};
         ${l} sourceLocB = coords;
-        --${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(N=>"int "+N),m=pn("sourceLocR",h-1).concat("inIdx.r"),A=pn("sourceLocG",h-1).concat("inIdx.g"),y=pn("sourceLocB",h-1).concat("inIdx.b"),g=pn("sourceLocA",h-1).concat("inIdx.a"),w=n==="max"?"greaterThan":"lessThan",b=r?"":`
+        --${c[o-2]};`;let d=["x","y","z","w","u","v"].slice(0,h),p="."+d[h-1],f=d.map(k=>"int "+k),m=pn("sourceLocR",h-1).concat("inIdx.r"),y=pn("sourceLocG",h-1).concat("inIdx.g"),A=pn("sourceLocB",h-1).concat("inIdx.b"),g=pn("sourceLocA",h-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=r?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
-                             getBestIndicesAChannel(${A.join()}),
                              getBestIndicesAChannel(${y.join()}),
-                             getBestIndicesAChannel(${g.join()})));`,_=`vec4(
+                             getBestIndicesAChannel(${A.join()}),
+                             getBestIndicesAChannel(${g.join()})));`,w=`vec4(
             getAChannel(${m.join()}),
-            hasNextCol ? getAChannel(${A.join()}) : 0.,
-            hasNextRow ? getAChannel(${y.join()}) : 0.,
-            hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,x=r?"":`
+            hasNextCol ? getAChannel(${y.join()}) : 0.,
+            hasNextRow ? getAChannel(${A.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,b=r?"":`
       float getBestIndicesAChannel(${f.join()}) {
         return getChannel(getBestIndicesA(${d.join()}),
                                           vec2(${d.slice(-2).join()}));
@@ -1291,7 +1291,7 @@ return log(x + sqrt(x * x - 1.0));`,iB=Xe({opSnippet:sB}),oB={kernelName:ao,back
         return getChannel(getA(${d.join()}),
                                vec2(${d.slice(-2).join()}));
       }
-      ${x}
+      ${b}
       void main() {
         ${l} coords = getOutputCoords();
         bool hasNextCol = ${c[o-1]} < ${i[o-1]-1};
@@ -1301,15 +1301,15 @@ return log(x + sqrt(x * x - 1.0));`,iB=Xe({opSnippet:sB}),oB={kernelName:ao,back
           sourceLocB${p}, sourceLocA${p}) * ${t};
         ivec4 inIdx = srcIdx;
         vec4 bestIndex = vec4(inIdx);
-        vec4 bestValue = ${_};
+        vec4 bestValue = ${w};
 
         for (int i = 0; i < ${t}; i++) {
           inIdx = srcIdx;
-          ${b}
-          vec4 candidate = ${_};
+          ${v}
+          vec4 candidate = ${w};
           bvec4 nan = isnan(candidate);
           bvec4 replace = bvec4(
-            vec4(${w}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
+            vec4(${x}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
 
           bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                            replace.y  ? candidate.y : bestValue.y,
@@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,iB=Xe({opSnippet:sB}),oB={kernelName:ao,back
         }
         setOutput(bestIndex);
       }
-    `}};function V3(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=R.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new yB(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=V3(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function j3(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=R.computeOptimalWindowSize(s),o=new gB(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=j3(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function U3(e,t,n,r){let a=[n];if(R.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=R.computeOutAndReduceShapes(t.shape,a),l=v.sizeFromShape(o),c=xe({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=V3(e,c,r);s.push(u);let h=xe({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return j3(e,t,r)}function xB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=En({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=U3(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var wB={kernelName:hs,backendName:"webgl",kernelFunc:xB};function bB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=v.parseAxisParam(s,a.shape),o=R.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=En({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=R.getInnerMostAxes(i.length,l.shape.length)),R.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=U3(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var _B={kernelName:yu,backendName:"webgl",kernelFunc:bB},vB=Nr+`
+    `}};function a7(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];r!=null&&(a=r.shape[0],s=r.shape[1]);let i=C.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new WB(o,n,r==null),c=[t];r!=null&&c.push(r);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let h=a7(e,t,n,u);return e.disposeIntermediateTensorInfo(u),h}function s7(e,t,n,r=null){let a=r!=null?r.shape:t.shape,s=a[a.length-1],i=C.computeOptimalWindowSize(s),o=new BB(a,i,n,r==null),l=r==null?[t]:[t,r],c=e.runWebGLProgram(o,l,"int32");if(c.shape.length===t.shape.length){let u=s7(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function i7(e,t,n,r){let a=[n];if(C.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=C.computeOutAndReduceShapes(t.shape,a),l=_.sizeFromShape(o),c=Ae({inputs:{x:t},backend:e,attrs:{shape:[-1,l]}});s.push(c);let u=a7(e,c,r);s.push(u);let h=Ae({inputs:{x:u},backend:e,attrs:{shape:i}});return s.forEach(d=>e.disposeIntermediateTensorInfo(d)),h}return s7(e,t,r)}function VB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let u=i7(n,l,i[0],"max");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var jB={kernelName:ps,backendName:"webgl",kernelFunc:VB};function UB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r,i=_.parseAxisParam(s,a.shape),o=C.getAxesPermutation(i,a.shape.length),l=a,c=[];o!=null&&(l=fn({inputs:{x:a},backend:n,attrs:{perm:o}}),c.push(l),i=C.getInnerMostAxes(i.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let u=i7(n,l,i[0],"min");return c.forEach(h=>n.disposeIntermediateTensorInfo(h)),u}var HB={kernelName:vu,backendName:"webgl",kernelFunc:UB},GB=Sr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,kB=Xe({opSnippet:vB}),IB={kernelName:so,backendName:"webgl",kernelFunc:kB},NB=Nr+"return log(x + sqrt(x * x + 1.0));",SB=Xe({opSnippet:NB}),TB={kernelName:io,backendName:"webgl",kernelFunc:SB},EB=Nr+`
+`,qB=Xe({opSnippet:GB}),XB={kernelName:co,backendName:"webgl",kernelFunc:qB},KB=Sr+"return log(x + sqrt(x * x + 1.0));",ZB=Xe({opSnippet:KB}),YB={kernelName:ho,backendName:"webgl",kernelFunc:ZB},JB=Sr+`
   return atan(x);
-`,CB=Xe({opSnippet:EB}),RB={kernelName:oo,backendName:"webgl",kernelFunc:CB},MB=LW+`
+`,QB=Xe({opSnippet:JB}),eV={kernelName:po,backendName:"webgl",kernelFunc:QB},tV=lB+`
   return atan(a, b);
-`,FB=`
+`,nV=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+WW+`
+  `+uB+`
   return result;
-`,DB=nn({opSnippet:MB,packedOpSnippet:FB}),$B={kernelName:uo,backendName:"webgl",kernelFunc:DB},OB=Nr+`
+`,rV=tn({opSnippet:tV,packedOpSnippet:nV}),aV={kernelName:mo,backendName:"webgl",kernelFunc:rV},sV=Sr+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelName:lo,backendName:"webgl",kernelFunc:zB},xc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,iV=Xe({opSnippet:sV}),oV={kernelName:fo,backendName:"webgl",kernelFunc:iV},kc=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,h=e.effectiveFilterWidth,d=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let k=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${p});
 
@@ -1378,16 +1378,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
               // use the current value.
               float currMinMaxValue = mix(
                   value, minMaxValue, minMaxValueFound);
-              if (value ${N} currMinMaxValue) {
+              if (value ${k} currMinMaxValue) {
                 minMaxValue = value;
                 minMaxValueFound = 1.0;
-                minMaxPosition = ${r?a?m:A:`wR * ${h} + wC`};
+                minMaxPosition = ${r?a?m:y:`wR * ${h} + wC`};
               }
             }
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let g="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,_=s%4,x=`
+      `;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,w=s%4,b=`
       if (${f}) {
         avgValue += dot(values, ones);
       } else {
@@ -1396,7 +1396,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
     `;this.userCode=`
       const ivec2 strides = ivec2(${i}, ${o});
       const ivec2 pads = ivec2(${d}, ${p});
-      const float initializationValue = ${y};
+      const float initializationValue = ${A};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float count = 0.0;
@@ -1420,7 +1420,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
         // max/min x(?, ?, d) to get y(yR, yC, d).
         // ? = to be determined
-        vec4 minMaxValue = vec4(${y});
+        vec4 minMaxValue = vec4(${A});
         float avgValue = 0.0;
         count = 0.0;
 
@@ -1432,7 +1432,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
             continue;
           }
 
-          for (int wC = 0; wC < ${b}; wC += 4) {
+          for (int wC = 0; wC < ${v}; wC += 4) {
             int xC = xCCorner + wC * ${c};
 
             vec4 values = vec4(
@@ -1442,11 +1442,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
               getValue(batch, xR, xC + 3 * ${c}, d)
             );
 
-            ${x}
+            ${b}
           }
 
-          int xC = xCCorner + ${b};
-          if (${_===1}) {
+          int xC = xCCorner + ${v};
+          if (${w===1}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               initializationValue,
@@ -1454,8 +1454,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
               initializationValue
             );
 
-            ${x}
-          } else if (${_===2}) {
+            ${b}
+          } else if (${w===2}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               getValue(batch, xR, xC + ${c}, d),
@@ -1463,8 +1463,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
               initializationValue
             );
 
-            ${x}
-          } else if (${_===3}) {
+            ${b}
+          } else if (${w===3}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               getValue(batch, xR, xC + ${c}, d),
@@ -1472,15 +1472,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
               initializationValue
             );
 
-            ${x}
+            ${b}
           }
         }
-        setOutput(${w});
+        setOutput(${x});
       }
-    `}},yA=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",w="0.0";if(g||(w="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
+    `}},Ey=class{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,h=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,y=e.padInfo.top,A=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
-        const ivec3 pads = ivec3(${m}, ${A}, ${y});
+        const ivec3 pads = ivec3(${m}, ${y}, ${A});
 
         void main() {
           ivec5 coords = getOutputCoords();
@@ -1539,17 +1539,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let b="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let x=Math.floor(s/4)*4,N=s%4,T=`
+      `;return}let v="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let b=Math.floor(s/4)*4,k=s%4,N=`
       if (${g}) {
         avgValue += dot(values, ones);
       } else {
-        minMaxValue = ${b}(values, minMaxValue);
+        minMaxValue = ${v}(values, minMaxValue);
       }
     `;this.userCode=`
       const ivec3 strides =
         ivec3(${i}, ${o}, ${l});
-      const ivec3 pads = ivec3(${m}, ${A}, ${y});
-      const float initializationValue = ${w};
+      const ivec3 pads = ivec3(${m}, ${y}, ${A});
+      const float initializationValue = ${x};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
       float count = 0.0;
@@ -1574,7 +1574,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
         // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
         // ? = to be determined
-        vec4 minMaxValue = vec4(${w});
+        vec4 minMaxValue = vec4(${x});
         float avgValue = 0.0;
         count = 0.0;
 
@@ -1594,7 +1594,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
               continue;
             }
 
-            for (int wC = 0; wC < ${x}; wC += 4) {
+            for (int wC = 0; wC < ${b}; wC += 4) {
               int xC = xCCorner + wC * ${h};
 
               vec4 values = vec4(
@@ -1604,11 +1604,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
                 getValue(batch, xD, xR, xC + 3 * ${h}, ch)
               );
 
-              ${T}
+              ${N}
             }
 
-            int xC = xCCorner + ${x};
-            if (${N===1}) {
+            int xC = xCCorner + ${b};
+            if (${k===1}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 initializationValue,
@@ -1616,8 +1616,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
                 initializationValue
               );
 
-              ${T}
-            } else if (${N===2}) {
+              ${N}
+            } else if (${k===2}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 getValue(batch, xD, xR, xC + ${h}, ch),
@@ -1625,8 +1625,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
                 initializationValue
               );
 
-              ${T}
-            } else if (${N===3}) {
+              ${N}
+            } else if (${k===3}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 getValue(batch, xD, xR, xC + ${h}, ch),
@@ -1634,13 +1634,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
                 initializationValue
               );
 
-              ${T}
+              ${N}
             }
           }
-          setOutput(${_});
+          setOutput(${w});
         }
       }
-    `}};function LB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Tl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:a},backend:n});let h=new xc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var WB={kernelName:ds,backendName:"webgl",kernelFunc:LB};function BB(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new yA(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var VB={kernelName:gu,backendName:"webgl",kernelFunc:BB},jB=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
+    `}};function lV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Vn({inputs:{x:a},backend:n});let h=new kc(u,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}var uV={kernelName:fs,backendName:"webgl",kernelFunc:lV};function cV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,l,c),d=new Ey(h,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}var hV={kernelName:ku,backendName:"webgl",kernelFunc:cV},dV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.top,u=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${c}, ${u});
       const float avgMultiplier = float(${h});
 
@@ -1682,9 +1682,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},UB=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,A=1/(t*n*r);this.userCode=`
+    `}},pV=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,h=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=u-1-e.padInfo.front,f=h-1-e.padInfo.top,m=d-1-e.padInfo.left,y=1/(t*n*r);this.userCode=`
       const ivec3 pads = ivec3(${p}, ${f}, ${m});
-      const float avgMultiplier = float(${A});
+      const float avgMultiplier = float(${y});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function HB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new UB(d);return n.runWebGLProgram(p,[a],i.dtype)}var GB={kernelName:Fh,backendName:"webgl",kernelFunc:HB};function qB(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Tl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=R.computePool2DInfo(i.shape,o,l,1,c),h=new jB(u);return n.runWebGLProgram(h,[a],i.dtype)}var XB={kernelName:Mh,backendName:"webgl",kernelFunc:qB};function KB(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return bp({a,b:s,transposeA:i,transposeB:o,backend:n})}var ZB={kernelName:ps,backendName:"webgl",kernelFunc:KB},YB=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function fV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new pV(d);return n.runWebGLProgram(p,[a],i.dtype)}var mV={kernelName:Oh,backendName:"webgl",kernelFunc:fV};function yV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;Fl([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:c}=r,u=C.computePool2DInfo(i.shape,o,l,1,c),h=new dV(u);return n.runWebGLProgram(h,[a],i.dtype)}var AV={kernelName:Dh,backendName:"webgl",kernelFunc:yV};function gV(e){let{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return Sp({a,b:s,transposeA:i,transposeB:o,backend:n})}var xV={kernelName:ms,backendName:"webgl",kernelFunc:gV},wV=class{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},JB=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],R.assertAndGetBroadcastShape(e,t),R.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(R.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(R.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},bV=class{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";r!=null&&(C.assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(C.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -1761,7 +1761,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},QB=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;v.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new JB(r.shape,a.shape,s.shape,u,h,l):new YB(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},eV={kernelName:Is,backendName:"webgl",kernelFunc:QB},nV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=`uniform int start[${this.rank}];`,r=tV(this.rank),a,s=e.map((i,o)=>`sourceLoc.${gA[o]} = start[${o}] + coords.${gA[o]};`);a=`
+    `}},_V=({inputs:e,backend:t,attrs:n})=>{let{x:r,mean:a,variance:s,offset:i,scale:o}=e;_.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),_.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),_.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[r,a,s],u=null;i!=null&&(u=i.shape,c.push(i));let h=null;o!=null&&(h=o.shape,c.push(o));let d=J().getBool("WEBGL_PACK_NORMALIZATION")?new bV(r.shape,a.shape,s.shape,u,h,l):new wV(r.shape,a.shape,s.shape,u,h,l);return t.runWebGLProgram(d,c,c[0].dtype)},vV={kernelName:Ns,backendName:"webgl",kernelFunc:_V},IV=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=`uniform int start[${this.rank}];`,r=kV(this.rank),a,s=e.map((i,o)=>`sourceLoc.${Cy[o]} = start[${o}] + coords.${Cy[o]};`);a=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${s.join(`
@@ -1772,7 +1772,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         ${a}
         setOutput(getSource(${r}));
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},gA=["x","y","z","w","u","v"];function tV(e){if(e===1)return"sourceLoc";if(e<=6)return gA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var rV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ot(this.rank),n=pn("coords",this.rank),r=pn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Cy=["x","y","z","w","u","v"];function kV(e){if(e===1)return"sourceLoc";if(e<=6)return Cy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var SV=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=lt(this.rank),n=pn("coords",this.rank),r=pn("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${r[this.rank-1]};
@@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         ${o}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function aV(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=un.computeFlatOffset(t,v.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function wc(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=un.parseSliceParams(a,s,i);if(un.assertParamsValid(a,o,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=JL(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=un.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rV(l):new nV(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),aV(a,o,l,n)}var sV={kernelName:Ho,backendName:"webgl",kernelFunc:wc},iV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;v.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,w)=>g*w),l=R.getReshaped(a.shape,s,o),c=R.getPermuted(l.length,s.length),u=R.getReshapedPermuted(a.shape,s,o),h=R.getSliceBeginCoords(i,s.length),d=R.getSliceSize(u,i,s.length),p=[],f=xe({inputs:{x:a},backend:n,attrs:{shape:l}}),m=En({inputs:{x:f},backend:n,attrs:{perm:c}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:u}}),y=wc({inputs:{x:A},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(A),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},oV={kernelName:xu,backendName:"webgl",kernelFunc:iV};function lV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=y3(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var uV={kernelName:Dh,backendName:"webgl",kernelFunc:lV},cV="return float(a != b);",H3=nn({opSnippet:cV,dtype:"bool"}),hV={kernelName:Fo,backendName:"webgl",kernelFunc:H3};function bc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Wn({inputs:{x:a.complexTensorInfos.real},backend:n})}var dV={kernelName:td,backendName:"webgl",kernelFunc:bc},pV="return float(int(x));";function fV(e,t){let n=new Ha(e.shape,pV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function xA(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Wn({inputs:{x:a},backend:n});let i=Ct(a.shape),o=xA({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=Ga({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=bc({inputs:{input:a},backend:n}),o=xA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!v.hasEncodingLoss(a.dtype,s)){let i=Wn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return fV(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),o=H3({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var mV={kernelName:fs,backendName:"webgl",kernelFunc:xA},G3="return ceil(x);",AV=Xe({opSnippet:G3,packedOpSnippet:G3,cpuKernelImpl:DL}),yV={kernelName:ms,backendName:"webgl",kernelFunc:AV},gV=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function NV(e,t,n,r){let a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=un.computeFlatOffset(t,_.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}function Ic(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=un.parseSliceParams(a,s,i);if(un.assertParamsValid(a,o,l),_.sizeFromShape(l)===0)return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=n.texData.get(a.dataId),d=bW(h.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,d)}let{isPacked:c}=n.texData.get(a.dataId),u=un.isSliceContinous(a.shape,o,l);if(c||!u){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SV(l):new IV(l),d=h.getCustomSetupFunc(o);return n.runWebGLProgram(h,[a],a.dtype,d)}return n.uploadToGPU(a.dataId),NV(a,o,l,n)}var TV={kernelName:Zo,backendName:"webgl",kernelFunc:Ic},EV=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;_.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),l=C.getReshaped(a.shape,s,o),c=C.getPermuted(l.length,s.length),u=C.getReshapedPermuted(a.shape,s,o),h=C.getSliceBeginCoords(i,s.length),d=C.getSliceSize(u,i,s.length),p=[],f=Ae({inputs:{x:a},backend:n,attrs:{shape:l}}),m=fn({inputs:{x:f},backend:n,attrs:{perm:c}}),y=Ae({inputs:{x:m},backend:n,attrs:{shape:u}}),A=Ic({inputs:{x:y},backend:n,attrs:{begin:h,size:d}});return p.push(f),p.push(m),p.push(y),p.forEach(g=>n.disposeIntermediateTensorInfo(g)),A},CV={kernelName:Iu,backendName:"webgl",kernelFunc:EV};function RV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),c=$3(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,c)}var MV={kernelName:zh,backendName:"webgl",kernelFunc:RV},FV="return float(a != b);",o7=tn({opSnippet:FV,dtype:"bool"}),$V={kernelName:Po,backendName:"webgl",kernelFunc:o7};function Sc(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Vn({inputs:{x:a.complexTensorInfos.real},backend:n})}var DV={kernelName:sd,backendName:"webgl",kernelFunc:Sc},OV="return float(int(x));";function zV(e,t){let n=new Ga(e.shape,OV),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}function Ry(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if(s==="complex64"){if(a.dtype==="complex64")return Vn({inputs:{x:a},backend:n});let i=Rt(a.shape),o=Ry({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),l=qa({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Sc({inputs:{input:a},backend:n}),o=Ry({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!_.hasEncodingLoss(a.dtype,s)){let i=Vn({inputs:{x:a},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return zV(a,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",_.getTypedArrayFromDType("bool",1)),o=o7({inputs:{a,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var PV={kernelName:ys,backendName:"webgl",kernelFunc:Ry},l7="return ceil(x);",LV=Xe({opSnippet:l7,packedOpSnippet:l7,cpuKernelImpl:rW}),WV={kernelName:As,backendName:"webgl",kernelFunc:LV},BV=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},xV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},VV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function wV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new xV(a.shape):o=new gV(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var bV={kernelName:Ca,backendName:"webgl",kernelFunc:wV},_V=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,r)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function jV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r,o;J().getBool("WEBGL_PACK_CLIP")?o=new VV(a.shape):o=new BV(a.shape);let l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}var UV={kernelName:Ra,backendName:"webgl",kernelFunc:jV},HV=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1842,7 +1842,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function q3(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function vV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new _V(r.shape),i=[q3(r,a.complexTensorInfos.real),q3(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var kV={kernelName:wu,backendName:"webgl",kernelFunc:vV},IV=class{constructor(e){this.outputShape=[],this.outputShape=R.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
+    `}};function u7(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function GV(e){let{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new HV(r.shape),i=[u7(r,a.complexTensorInfos.real),u7(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var qV={kernelName:Su,backendName:"webgl",kernelFunc:GV},XV=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -1851,18 +1851,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         ${n.join(`
         `)}
       }
-    `}},NV=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=R.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=ot(r),s=pn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
+    `}},KV=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,r=n.length,a=lt(r),s=pn("coords",r),i=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],c=i.slice(-2),u=i.join(),h=`if (${l} < ${o[0]}) {
         return getChannel(
             getT0(${u}), vec2(${c.join()}));
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
         if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
           return getChannel(
-            getT${f}(${vp(i,l,m)}),
-            vec2(${vp(c,l,m)}));
+            getT${f}(${Tp(i,l,m)}),
+            vec2(${Tp(c,l,m)}));
         }`}let d=o.length,p=o[o.length-1];h+=`
         return getChannel(
-          getT${d}(${vp(i,l,p)}),
-          vec2(${vp(c,l,p)}));`,this.userCode=`
+          getT${d}(${Tp(i,l,p)}),
+          vec2(${Tp(c,l,p)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${h}
       }
@@ -1888,18 +1888,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(result);
       }
-    `}};function vp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function kp(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Wn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var SV={kernelName:Xh,backendName:"webgl",kernelFunc:kp};function zl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>bc({inputs:{input:f},backend:n})),u=e.map(f=>kp({inputs:{input:f},backend:n})),h=zl(c,t,n),d=zl(u,t,n),p=Ga({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=X3(e,t,n),h=c.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),d=c[0].shape[0]===1,p=$L(h,u,r,d),f=R.computeOutShape(e.map(A=>A.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(A=>n.disposeIntermediateTensorInfo(A)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=zl(e.slice(0,c),t,n),h=zl(e.slice(c),t,n),d=zl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new NV(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=X3(e,t,n),i=new IV(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=xe({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function X3(e,t,n){let r=R.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>xe({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function K3(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=v.parseAxisParam(a,t[0].shape)[0],i=R.computeOutShape(t.map(c=>c.shape),s);if(v.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>v.sizeFromShape(c.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return R.assertParamsConsistent(l,s),zl(o,s,n)}var TV={kernelName:co,backendName:"webgl",kernelFunc:K3},Z3=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",A=m?1:2,y=m?2:3,g=m?3:1,w="",b="";n&&(r?w=`float activation(float a) {
+    `}};function Tp(e,t,n){let r=e.indexOf(t);return e.map((a,s)=>s===r?`${a} - ${n}`:a).join()}function Ep(e){let{inputs:t,backend:n}=e,{input:r}=t,a=n.texData.get(r.dataId);return Vn({inputs:{x:a.complexTensorInfos.imag},backend:n})}var ZV={kernelName:Jh,backendName:"webgl",kernelFunc:Ep};function Vl(e,t,n){let r=e[0].dtype;if(r==="complex64"){let c=e.map(f=>Sc({inputs:{input:f},backend:n})),u=e.map(f=>Ep({inputs:{input:f},backend:n})),h=Vl(c,t,n),d=Vl(u,t,n),p=qa({inputs:{real:h,imag:d},backend:n});return c.forEach(f=>n.disposeIntermediateTensorInfo(f)),u.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),p}if(r==="string"){let{tensors2D:c,outShape:u}=c7(e,t,n),h=c.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),d=c[0].shape[0]===1,p=aW(h,u,r,d),f=C.computeOutShape(e.map(y=>y.shape),t),m=n.makeTensorInfo(f,r,p);return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),m}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),u=Vl(e.slice(0,c),t,n),h=Vl(e.slice(c),t,n),d=Vl([u,h],t,n);return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),d}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new KV(e.map(u=>u.shape),t);return n.runWebGLProgram(c,e,r)}let{tensors2D:a,outShape:s}=c7(e,t,n),i=new XV(a.map(c=>c.shape)),o=n.runWebGLProgram(i,a,r);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=Ae({inputs:{x:o},attrs:{shape:s},backend:n});return n.disposeIntermediateTensorInfo(o),l}function c7(e,t,n){let r=C.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ae({inputs:{x:a},attrs:{shape:[-1,_.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:r}}function h7(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=_.parseAxisParam(a,t[0].shape)[0],i=C.computeOutShape(t.map(c=>c.shape),s);if(_.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(c=>_.sizeFromShape(c.shape)>0);if(o.length===1)return Vn({inputs:{x:o[0]},backend:n});let l=o.map(c=>c.shape);return C.assertParamsConsistent(l,s),Vl(o,s,n)}var YV={kernelName:yo,backendName:"webgl",kernelFunc:h7},d7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",y=m?1:2,A=m?2:3,g=m?3:1,x="",v="";n&&(r?x=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
-        }`:a?w=`float activation(float a) {
+        }`:a?x=`float activation(float a) {
           float b = getLeakyreluAlphaAtOutCoords();
           ${n}
-        }`:w=`
+        }`:x=`
           float activation(float x) {
             ${n}
           }
-        `,b="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
-      ${w}
+        `,v="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${x}
 
       const ivec2 strides = ivec2(${o}, ${l});
       const ivec2 pads = ivec2(${s}, ${i});
@@ -1910,7 +1910,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         int d2 = coords[${g}];
 
         ivec2 xRCCorner =
-            ivec2(coords[${A}], coords[${y}]) * strides - pads;
+            ivec2(coords[${y}], coords[${A}]) * strides - pads;
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
 
@@ -2018,11 +2018,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
 
         float result = dotProd;
-        ${_}
-        ${b}
+        ${w}
+        ${v}
         setOutput(result);
       }
-    `}},EV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+    `}},JV=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,h=e.filterHeight,d=e.filterWidth,p=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${a}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${n}, ${r});
 
@@ -2110,15 +2110,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},CV=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=dn(),A=h==="channelsLast",y=A?0:1,g=A?1:2,w="";for(let b=0;b<=1;b++)for(let _=0;_<=1;_++)w+=`
-          blockIndex = rc.y + ${_};
-          pos = rc.x + ${b};
+    `}},QV=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:c,dilationHeight:u,dataFormat:h}=n,{left:d,top:p}=o,f=a*r,m=dn(),y=h==="channelsLast",A=y?0:1,g=y?1:2,x="";for(let v=0;v<=1;v++)for(let w=0;w<=1;w++)x+=`
+          blockIndex = rc.y + ${w};
+          pos = rc.x + ${v};
 
           if(blockIndex < ${e[1]} && pos < ${e[0]}) {
             offsetY = int(blockIndex / (${l})) * ${i} - ${p};
             d0 = offsetY + ${u} * (pos / ${f});
 
-            if(d0 < ${t[y]} && d0 >= 0) {
+            if(d0 < ${t[A]} && d0 >= 0) {
 
               offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${d}.);
               d1 = offsetX + ${c} * (int(mod(float(pos), ${f}.) / ${a}.));
@@ -2127,14 +2127,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
                 ch = int(mod(float(pos), ${a}.));
 
-                if (${A}) {
+                if (${y}) {
                   innerDims = vec2(d1, ch);
-                  result[${b*2+_}] = getChannel(
+                  result[${v*2+w}] = getChannel(
                     getA(d0, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 } else {
                   innerDims = vec2(d0, d1);
-                  result[${b*2+_}] = getChannel(
+                  result[${v*2+w}] = getChannel(
                     getA(ch, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 }
@@ -2150,11 +2150,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
         vec2 innerDims;
 
-        ${w}
+        ${x}
 
         ${m.output} = result;
       }
-    `}};function Y3({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,A,y=[],g=(h===1||d===1)&&u>L3,w=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let b=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],_=xe({inputs:{x:e},backend:r,attrs:{shape:[1,b,n.inChannels]}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=bp({a:_,b:x,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=xe({inputs:{x:N},backend:r,attrs:{shape:n.outShape}}),y.push(_),y.push(x),y.push(N)}else{let b=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),_={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},x=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(pc(c.shape,_.shape),()=>`packed reshape ${c.shape} to ${_.shape} isn't free`);let N=xe({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let T=bp({a:_,b:N,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(T.dataId);v.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=x,E.shape=n.outShape,A=Wn({inputs:{x:T},backend:r}),A.shape=n.outShape,y.push(T)}for(let b of y)r.disposeIntermediateTensorInfo(b);return A}function J3({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,A=d*h,y=[m,A],g=!0,w=!1,b=[],_=xe({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),x=xe({inputs:{x:t},backend:r,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(_),b.push(x);let N=new CV(y,_.shape,n),T=r.runWebGLProgram(N,[_],"float32"),E=xe({inputs:{x:T},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(T),b.push(E);let F=a!=null,$=s!=null,L=o==="leakyrelu",V=o?xp(o,!0):null,j=new F3(E.shape,x.shape,[1,A,n.outChannels],g,w,F,V,$,L),U=[E,x];if(a&&U.push(a),$&&U.push(s),L){let Y=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));U.push(Y),b.push(Y)}let X=r.runWebGLProgram(j,U,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=xe({inputs:{x:X},backend:r,attrs:{shape:G}});b.push(X);for(let Y of b)r.disposeIntermediateTensorInfo(Y);return ee}function RV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=Y3({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=J3({x:a,filter:s,convInfo:d,backend:n});else{let m=new Z3(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=xe({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var MV={kernelName:As,backendName:"webgl",kernelFunc:RV},FV=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function p7({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,c=r.texData.get(e.dataId),u=n.inChannels,h=l[0]*l[1]*l[2],d=n.outChannels,p=n.dataFormat==="channelsLast",f=!1,m=!1,y,A=[],g=(h===1||d===1)&&u>t7,x=l[2]%2!=0&&!!c.isPacked;if(g||!J().getBool("WEBGL_LAZILY_UNPACK")||!J().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=p?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=Ae({inputs:{x:e},backend:r,attrs:{shape:[1,v,n.inChannels]}}),b=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),k=Sp({a:w,b,transposeA:f,transposeB:m,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});y=Ae({inputs:{x:k},backend:r,attrs:{shape:n.outShape}}),A.push(w),A.push(b),A.push(k)}else{let v=p?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),w={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},b=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,_.assert(gc(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let k=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(k);let N=Sp({a:w,b:k,backend:r,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=r.texData.get(N.dataId);_.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=b,E.shape=n.outShape,y=Vn({inputs:{x:N},backend:r}),y.shape=n.outShape,A.push(N)}for(let v of A)r.disposeIntermediateTensorInfo(v);return y}function f7({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:h,outHeight:d,dataFormat:p}=n,f=p==="channelsLast",m=l*c*u,y=d*h,A=[m,y],g=!0,x=!1,v=[],w=Ae({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),b=Ae({inputs:{x:t},backend:r,attrs:{shape:[1,m,_.sizeFromShape(t.shape)/m]}});v.push(w),v.push(b);let k=new QV(A,w.shape,n),N=r.runWebGLProgram(k,[w],"float32"),E=Ae({inputs:{x:N},backend:r,attrs:{shape:[1,A[0],A[1]]}});v.push(N),v.push(E);let F=a!=null,O=s!=null,L=o==="leakyrelu",V=o?vp(o,!0):null,j=new Z3(E.shape,b.shape,[1,y,n.outChannels],g,x,F,V,O,L),U=[E,b];if(a&&U.push(a),O&&U.push(s),L){let Y=r.makeTensorInfo([],"float32",_.createScalarValue(i,"float32"));U.push(Y),v.push(Y)}let X=r.runWebGLProgram(j,U,"float32"),G=f?[1,d,h,n.outChannels]:[1,n.outChannels,d,h],ee=Ae({inputs:{x:X},backend:r,attrs:{shape:G}});v.push(X);for(let Y of v)r.disposeIntermediateTensorInfo(Y);return ee}function ej(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,s.shape,i,c,o,u,!1,h),p;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))p=p7({x:a,filter:s,convInfo:d,backend:n});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)p=f7({x:a,filter:s,convInfo:d,backend:n});else{let m=new d7(d);p=n.runWebGLProgram(m,[a,s],"float32")}let f=Ae({inputs:{x:p},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(p),f}var tj={kernelName:gs,backendName:"webgl",kernelFunc:ej},nj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2196,7 +2196,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},DV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
+    `}},rj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,c=s?2:3,u=s?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},$V=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},aj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2291,7 +2291,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},OV=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
+    `}},sj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=r-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${o}, ${l}, ${c});
 
       void main() {
@@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function zV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=R.convertConv2DDataFormat(l),d=R.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new FV(d);return n.runWebGLProgram(p,[a,s],"float32")}var PV={kernelName:Oh,backendName:"webgl",kernelFunc:zV};function LV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=R.convertConv2DDataFormat(c),d=R.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new DV(d);return n.runWebGLProgram(p,[a,s],"float32")}var WV={kernelName:ys,backendName:"webgl",kernelFunc:LV};function BV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new EV(c);return n.runWebGLProgram(u,[a,s],"float32")}var VV={kernelName:bu,backendName:"webgl",kernelFunc:BV};function jV(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=R.computeConv3DInfo(a.shape,l,i,1,o),u=new $V(c);return n.runWebGLProgram(u,[a,s],"float32")}var UV={kernelName:zh,backendName:"webgl",kernelFunc:jV};function HV(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=R.computeConv3DInfo(l,s.shape,o,1,i),u=new OV(c);return n.runWebGLProgram(u,[a,s],"float32")}var GV={kernelName:Ph,backendName:"webgl",kernelFunc:HV},qV=M3+`
+    `}};function ij(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:c,filterShape:u}=r,h=C.convertConv2DDataFormat(l),d=C.computeConv2DInfo(a.shape,u,i,1,o,c,!1,h),p=new nj(d);return n.runWebGLProgram(p,[a,s],"float32")}var oj={kernelName:Lh,backendName:"webgl",kernelFunc:ij};function lj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:c,dimRoundingMode:u}=r,h=C.convertConv2DDataFormat(c),d=C.computeConv2DInfo(i,s.shape,o,1,l,u,!1,h),p=new rj(d);return n.runWebGLProgram(p,[a,s],"float32")}var uj={kernelName:xs,backendName:"webgl",kernelFunc:lj};function cj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeConv3DInfo(a.shape,s.shape,i,l,o),u=new JV(c);return n.runWebGLProgram(u,[a,s],"float32")}var hj={kernelName:Nu,backendName:"webgl",kernelFunc:cj};function dj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,c=C.computeConv3DInfo(a.shape,l,i,1,o),u=new aj(c);return n.runWebGLProgram(u,[a,s],"float32")}var pj={kernelName:Wh,backendName:"webgl",kernelFunc:dj};function fj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,c=C.computeConv3DInfo(l,s.shape,o,1,i),u=new sj(c);return n.runWebGLProgram(u,[a,s],"float32")}var mj={kernelName:Bh,backendName:"webgl",kernelFunc:fj},yj=K3+`
   return cos(x);
-`,XV=Xe({opSnippet:qV}),KV={kernelName:gs,backendName:"webgl",kernelFunc:XV},ZV=`
+`,Aj=Xe({opSnippet:yj}),gj={kernelName:ws,backendName:"webgl",kernelFunc:Aj},xj=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,YV=Xe({opSnippet:ZV}),JV={kernelName:ho,backendName:"webgl",kernelFunc:YV},QV=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,A,y]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,w,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,wj=Xe({opSnippet:xj}),bj={kernelName:Ao,backendName:"webgl",kernelFunc:wj},_j=class{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[c]=t,[u,h]=n;this.outputShape=[c,u,h,l];let d=r==="bilinear"?1:0,[p,f]=[`${i-1}.0`,`${o-1}.0`],[m,y,A]=u>1?[`${(i-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${p} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${p}`],[g,x,v]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${g});
       void main() {
@@ -2375,15 +2375,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
           return;
         }
 
-        float height_scale = ${A};
-        float width_scale = ${w};
+        float height_scale = ${y};
+        float width_scale = ${x};
 
-        float in_y = ${y};
+        float in_y = ${A};
         if( in_y < 0.0 || in_y > ${p} ) {
           setOutput(float(${a}));
           return;
         }
-        float in_x = ${b};
+        float in_x = ${v};
         if( in_x < 0.0 || in_x > ${f} ) {
           setOutput(float(${a}));
           return;
@@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
           setOutput(newValue);
         }
       }
-    `}},ej=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new QV(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},tj={kernelName:po,backendName:"webgl",kernelFunc:ej},t7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${Q3(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},vj=e=>{let{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:c}=r,u=new _j(a.shape,s.shape,o,l,c);return n.runWebGLProgram(u,[a,s,i],"float32")},kj={kernelName:go,backendName:"webgl",kernelFunc:vj},A7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let r=e.length,a=t?"0.0":`getX(${m7(r,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
-        ${ot(r)} coords = getOutputCoords();
-        int end = ${e7(r,"coords")};
+        ${lt(r)} coords = getOutputCoords();
+        int end = ${y7(r,"coords")};
         float val = ${a};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${e7(r,"coords")} = idx;
-          val += getX(${Q3(r,"coords")});
+          ${y7(r,"coords")} = idx;
+          val += getX(${m7(r,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function Q3(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function e7(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function nj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=R.getAxesPermutation([s],l),u=a;c!=null&&(u=En({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=R.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Wn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new t7(u.shape,!1,o),A=m.getCustomSetupFunc(f),y=p;p=n.runWebGLProgram(m,[p],p.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let f=new t7(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=R.getUndoAxesPermutation(c),m=En({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var rj={kernelName:xs,backendName:"webgl",kernelFunc:nj};function aj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=y3(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=FL(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var sj={kernelName:Lh,backendName:"webgl",kernelFunc:aj},ij=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function m7(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function y7(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Ij(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,c=C.getAxesPermutation([s],l),u=a;c!=null&&(u=fn({inputs:{x:a},backend:n,attrs:{perm:c}}));let h=C.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let d=u.shape[h],p=Vn({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new A7(u.shape,!1,o),y=m.getCustomSetupFunc(f),A=p;p=n.runWebGLProgram(m,[p],p.dtype,y),n.disposeIntermediateTensorInfo(A)}if(i){let f=new A7(u.shape,i,o),m=p;p=n.runWebGLProgram(f,[p],p.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=C.getUndoAxesPermutation(c),m=fn({inputs:{x:p},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(u),m}return p}var Sj={kernelName:bs,backendName:"webgl",kernelFunc:Ij};function Nj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(a.shape.length===1){let l=n.readSync(a.dataId),c=n.readSync(s.dataId),u=$3(l,c,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}else if(a.shape.length===2){let l=n.bufferSync(a),c=n.bufferSync(s),u=nW(l,c,i,o);return n.makeTensorInfo(u.shape,s.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Tj={kernelName:Vh,backendName:"webgl",kernelFunc:Nj},Ej=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2447,18 +2447,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function oj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;v.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new ij(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var lj={kernelName:fo,backendName:"webgl",kernelFunc:oj},n7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,A="",y="";n&&(r?A=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Cj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;_.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],c=i==="NHWC"?a.shape[2]:a.shape[3],u=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,d=c*s,p=u/(s*s),f=i==="NHWC"?[o,h,d,p]:[o,p,h,d],m=new Ej(f,s,i);return n.runWebGLProgram(m,[a],a.dtype)}var Rj={kernelName:xo,backendName:"webgl",kernelFunc:Cj},g7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=e.outChannels/e.inChannels,y="",A="";n&&(r?y=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
-        }`:a?A=`float activation(float a) {
+        }`:a?y=`float activation(float a) {
           float b = getLeakyreluAlphaAtOutCoords();
           ${n}
-        }`:A=`
+        }`:y=`
           float activation(float x) {
             ${n}
           }
-        `,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
-      ${A}
+        `,A="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${y}
 
       const ivec2 strides = ivec2(${c}, ${u});
       const ivec2 pads = ivec2(${o}, ${l});
@@ -2500,141 +2500,159 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
         float result = dotProd;
         ${g}
-        ${y}
+        ${A}
         setOutput(result);
       }
-    `}},r7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,c=e.strideHeight,u=e.strideWidth,h=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,f=e.filterWidth,m=f,A="int xR; int xC; int xCOffset;";for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`
-          vec4 xTexelR${b}C${_*2} = vec4(0.);
-          vec4 wR${b}C${_} = vec4(0.);
-          vec4 xR${b}C${_} = vec4(0.);`;for(let b=0;b<p;b++)for(let _=0;_<m;_++){let x=_*2;if(A+=`
-          xR = xRCorner + ${b*h};
-          xC = xCCorner + ${x*d};
-        `,u===1){if(x<f&&(l%2==1?A+=`
+    `}},x7=class{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,c=e.padInfo.left,u=e.strideHeight,h=e.strideWidth,d=e.dilationHeight,p=e.dilationWidth,f=e.filterHeight,m=e.filterWidth,y=m,A=`
+      int xR; int xC; int xCOffset;
+      vec4 wTexel; vec4 previous; vec4 final;`;for(let w=0;w<m;w++)A+=`
+          vec4 xTexelC${w*2};
+          vec4 xC${w};`;for(let w=0;w<f;w++){for(let b=0;b<m;b++)A+=`
+          xTexelC${b*2} = vec4(0.0);
+          xC${b} = vec4(0.0);`;A+=`
+        xR = xRCorner + ${w*d};
+        if (xR >=0 && xR < ${i}) {
+      `;for(let b=0;b<y/2+1;b++){let k=b*2;if(A+=`
+          xC = xCCorner + ${k*p};
+          `,h===1){if(k<m&&(c%2==1?(A+=`
                 xCOffset = xC + 1;
-                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
+                if (xCOffset >= 0 && xCOffset < ${o}) {
+                  xTexelC${k} = getX(batch, xR, xCOffset, d1);
 
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${i}) {
-                    xTexelR${b}C${x}.zw = vec2(0.);
+                  if (xCOffset + 1 >= ${o}) {
+                    xTexelC${k}.zw = vec2(0.0);
                   }
-                } else {
-                  xTexelR${b}C${x} = vec4(0.);
                 }
+              `,p===1&&k>0?A+=`
+                xC${k} = vec4(xTexelC${k-2}.zw, xTexelC${k}.xy);
+                `:A+=`
+                  xCOffset = xC + 1 - 2;
 
-                xCOffset = xC + 1 - 2;
-                if(xR >= 0 && xR < ${s} && xCOffset >= 0 && xCOffset < ${i}) {
-                  vec4 previous = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset >= 0 && xCOffset < ${o}) {
+                    previous = getX(batch, xR, xCOffset, d1);
 
-                  // Need to manually clear unused channels in case
-                  // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${i}) {
-                    previous.zw = vec2(0.);
-                  }
-
-                  xR${b}C${x} = vec4(previous.zw, xTexelR${b}C${x}.xy);
-                } else {
-                  xR${b}C${x} = vec4(0, 0, xTexelR${b}C${x}.xy);
-                }
-              `:A+=`
-                if(xR >= 0 && xR < ${s} && xC >= 0 && xC < ${i}) {
-                  xTexelR${b}C${x} = getX(batch, xR, xC, d1);
-                } else {
-                  xTexelR${b}C${x} = vec4(0.);
-                }
-
-                xR${b}C${x} = xTexelR${b}C${x};
-              `,x+1<f)){let N=l%2==0?v.nearestLargerEven(d):d;d%2==0&&l%2==1||d%2!=0&&l%2!=1?(A+=`
-                  xCOffset = xC + ${l%2} + ${N};
-
-                  if(xR >= 0 && xR < ${s} &&
-                    xCOffset >= 0 && xCOffset < ${i}) {
-                    xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
-                  }
-                `,d>1&&(A+=`
-                    xCOffset -= 2;
-                    if(xR >= 0 && xR < ${s} &&
-                      xCOffset >= 0 && xCOffset < ${i}) {
-                      xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
-                    } else {
-                      xTexelR${b}C${x} = vec4(0.);
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= ${o}) {
+                      previous.zw = vec2(0.0);
                     }
-                  `),A+=`
-                  xR${b}C${x+1} = vec4(
-                    xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.xy);
-                `):A+=`
-                  xCOffset = xC + ${N};
 
-                  if(xR >= 0 && xR < ${s} &&
-                    xCOffset >= 0 && xCOffset < ${i}) {
-                    xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
+                    xC${k} = vec4(previous.zw, xTexelC${k}.xy);
+                  } else {
+                    xC${k} = vec4(0.0, 0.0, xTexelC${k}.xy);
+                  }
+                  `):A+=`
+                if (xC >= 0 && xC < ${o}) {
+                  xTexelC${k} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= ${o}) {
+                    xTexelC${k}.zw = vec2(0.0);
                   }
-
-                  xR${b}C${x+1} = xTexelR${b}C${x+2};
-                `}}else x<f&&(A+=`
-              if(xR >= 0 && xR < ${s}) {
-            `,l%2==1?(A+=`
-                xCOffset = xC + 1 - ${u};
-                if(xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${b}C${x} = getX(batch, xR, xCOffset, d1);
-                } else {
-                  xTexelR${b}C${x} = vec4(0.);
                 }
 
-                if(xC + 1 >= 0 && xC + 1 < ${i}) {
-                  xTexelR${b}C${x+2} = getX(batch, xR, xC + 1, d1);
-                } else {
-                  xTexelR${b}C${x+2} = vec4(0.);
+                xC${k} = xTexelC${k};
+                `,k+1<m)){let N=c%2==0?_.nearestLargerEven(p):p;p%2==0&&c%2==1||p%2!=0&&c%2!=1?(A+=`
+                  xCOffset = xC + ${c%2} + ${N};
+
+                  if (xCOffset >= 0 && xCOffset < ${o}) {
+                    xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= ${o}) {
+                      xTexelC${k+2}.zw = vec2(0.0);
+                    }
+                  }
+                  `,p>1&&(A+=`
+                    xCOffset -= 2;
+                    if (xCOffset >= 0 && xCOffset < ${o}) {
+                      xTexelC${k} = getX(batch, xR, xCOffset, d1);
+                    }
+                    `),A+=`
+                  xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.xy);
+                  `):N===1?A+=`
+                    xC${k+1} = xTexelC${k};
+                    `:A+=`
+                    xCOffset = xC + ${N};
+
+                    if (xCOffset >= 0 && xCOffset < ${o}) {
+                      xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
+                      if (xCOffset + 1 >= ${o}) {
+                        xTexelC${k+2}.zw = vec2(0.0);
+                      }
+                    }
+
+                    xC${k+1} = xTexelC${k+2};
+                    `}}else k<m&&(c%2==1?(A+=`
+                xCOffset = xC + 1 - ${h};
+                if(xCOffset >= 0 && xCOffset < ${o}) {
+                  xTexelC${k} = getX(batch, xR, xCOffset, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= ${o}) {
+                    xTexelC${k}.zw = vec2(0.0);
+                  }
                 }
 
-                xR${b}C${x} = vec4(
-                  xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
-              `,x+1<f&&(A+=`
-                  vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${u};
-                  if(xCOffset >= 0 && xCOffset < ${i}) {
+                if(xC + 1 >= 0 && xC + 1 < ${o}) {
+                  xTexelC${k+2} = getX(batch, xR, xC + 1, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xC + 2 >= ${o}) {
+                    xTexelC${k+2}.zw = vec2(0.0);
+                  }
+                }
+
+                xC${k} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
+              `,k+1<m&&(A+=`
+                  final = vec4(0.0);
+                  xCOffset = xC + 1 + ${h};
+                  if(xCOffset >= 0 && xCOffset < ${o}) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
-                  xR${b}C${x+1} = vec4(xTexelR${b}C${x+2}.xy, final.xy);
+                  xC${k+1} = vec4(xTexelC${k+2}.xy, final.xy);
                 `)):(A+=`
-                if(xC >= 0 && xC < ${i}) {
-                  xTexelR${b}C${x} = getX(batch, xR, xC, d1);
-                } else {
-                  xTexelR${b}C${x} = vec4(0.);
+                if(xC >= 0 && xC < ${o}) {
+                  xTexelC${k} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= ${o}) {
+                    xTexelC${k}.zw = vec2(0.0);
+                  }
                 }
 
-                xCOffset = xC + ${u};
-                if(xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${b}C${x+2} = getX(batch, xR, xCOffset, d1);
-                } else {
-                  xTexelR${b}C${x+2} = vec4(0.);
+                xCOffset = xC + ${h};
+                if(xCOffset >= 0 && xCOffset < ${o}) {
+                  xTexelC${k+2} = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset + 1 >= ${o}) {
+                    xTexelC${k+2}.zw = vec2(0.);
+                  }
                 }
 
-                xR${b}C${x} = vec4(
-                  xTexelR${b}C${x}.xy, xTexelR${b}C${x+2}.xy);
-              `,x+1<f&&(A+=`
-                  xR${b}C${x+1} = vec4(
-                    xTexelR${b}C${x}.zw, xTexelR${b}C${x+2}.zw);
-                `)),A+="}");x<f&&(A+=`
-            vec4 wTexelR${b}C${x} = getW(${b}, ${x}, d1, q);
-            wR${b}C${x} = vec4(wTexelR${b}C${x}.xz, wTexelR${b}C${x}.xz);
-          `,x+1<f&&(A+=`
-              vec4 wTexelR${b}C${x+1} = getW(${b}, ${x+1}, d1, q);
-              wR${b}C${x+1} =
-                vec4(wTexelR${b}C${x+1}.xz, wTexelR${b}C${x+1}.xz);`))}for(let b=0;b<p;b++)for(let _=0;_<f;_++)A+=`dotProd += xR${b}C${_} * wR${b}C${_};`;let y="",g="";n&&(r?y=`vec4 activation(vec4 a) {
+                xC${k} = vec4(
+                  xTexelC${k}.xy, xTexelC${k+2}.xy);
+              `,k+1<m&&(A+=`
+                  xC${k+1} = vec4(xTexelC${k}.zw, xTexelC${k+2}.zw);
+                `)));k<m&&(A+=`
+            wTexel = getW(${w}, ${k}, d1, q);
+            dotProd += xC${k} * vec4(wTexel.xz, wTexel.xz);
+          `,k+1<m&&(A+=`
+              wTexel = getW(${w}, ${k+1}, d1, q);
+              dotProd += xC${k+1} * vec4(wTexel.xz, wTexel.xz);
+            `))}A+=`
+        }
+      `}let g="",x="";n&&(r?g=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${n}
-        }`:a?y=`vec4 activation(vec4 a) {
+        }`:a?g=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${n}
-        }`:y=`vec4 activation(vec4 x) {
+        }`:g=`vec4 activation(vec4 x) {
           ${n}
-        }`,g="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
-      ${y}
+        }`,x="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${g}
 
-      const ivec2 strides = ivec2(${c}, ${u});
-      const ivec2 pads = ivec2(${o}, ${l});
+      const ivec2 strides = ivec2(${u}, ${h});
+      const ivec2 pads = ivec2(${l}, ${c});
 
       void main() {
 
@@ -2642,21 +2660,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
-        int d1 = d2;
-        int q = 0;
+        int d1 = d2 / ${s};
+        int q = d2 - d1 * ${s};
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
 
-        vec4 dotProd = vec4(0.);
+        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
+        vec4 dotProd = vec4(0.000000000000001);
 
         ${A}
 
-        vec4 result = dotProd;
-        ${w}
-        ${g}
+        vec4 result = dotProd - vec4(0.000000000000001);
+        ${v}
+        ${x}
         setOutput(result);
       }
-    `}};function uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=R.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new r7(h):d=new n7(h),n.runWebGLProgram(d,[a,s],"float32")}var cj={kernelName:ws,backendName:"webgl",kernelFunc:uj},hj=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function Mj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:c}=r,u=l;u==null&&(u=[1,1]),_.assert(C.eitherStridesOrDilationsAreOne(i,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let h=C.computeConv2DInfo(a.shape,s.shape,i,u,o,c,!0),d;return J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?d=new x7(h):d=new g7(h),n.runWebGLProgram(d,[a,s],"float32")}var Fj={kernelName:_s,backendName:"webgl",kernelFunc:Mj},$j=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2691,7 +2710,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},dj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},Dj=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -2736,13 +2755,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function pj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=R.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new hj(h);return n.runWebGLProgram(d,[a,s],"float32")}var fj={kernelName:Wh,backendName:"webgl",kernelFunc:pj};function mj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=R.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new dj(h);return n.runWebGLProgram(d,[a,s],"float32")}var Aj={kernelName:Bh,backendName:"webgl",kernelFunc:mj},yj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function Oj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,filterShape:u}=r,h=C.computeConv2DInfo(a.shape,u,i,o,l,c,!0),d=new $j(h);return n.runWebGLProgram(d,[a,s],"float32")}var zj={kernelName:jh,backendName:"webgl",kernelFunc:Oj};function Pj(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:c,inputShape:u}=r,h=C.computeConv2DInfo(u,s.shape,i,o,l,c,!0),d=new Dj(h);return n.runWebGLProgram(d,[a,s],"float32")}var Lj={kernelName:Uh,backendName:"webgl",kernelFunc:Pj},Wj=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function gj(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=v.sizeFromShape(r.shape),i=xe({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new yj(s),l=n.runWebGLProgram(o,[i],i.dtype),c=xe({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var xj={kernelName:Vh,backendName:"webgl",kernelFunc:gj},wj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
+    `}};function Bj(e){let{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=_.sizeFromShape(r.shape),i=Ae({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new Wj(s),l=n.runWebGLProgram(o,[i],i.dtype),c=Ae({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),c}var Vj={kernelName:Hh,backendName:"webgl",kernelFunc:Bj},jj=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:c}=e,{top:u,left:h}=r;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
       const ivec2 pads = ivec2(${u}, ${h});
       const float neg_infinity = -3.4e38;
@@ -2780,7 +2799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function bj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=R.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new wj(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=xe({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var _j={kernelName:_u,backendName:"webgl",kernelFunc:bj},vj="return (x >= 0.0) ? x : (exp(x) - 1.0);",kj=`
+    `}};function Uj(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,c=C.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),u,h=new jj(c);u=n.runWebGLProgram(h,[a,s],"float32");let d=Ae({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),d}var Hj={kernelName:Tu,backendName:"webgl",kernelFunc:Uj};function Gj(e){let{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=C.decodeEinsumEquation(a,s.length);C.checkEinsumDimSizes(i.length,l,s);let{path:c,steps:u}=C.getEinsumComputePath(o,l),h=u.length,d=null,p=i.length,f=[];for(let m=0;m<h;++m){for(let y of u[m]){let{permutationIndices:A,expandDims:g}=C.getEinsumPermutation(p,l[y]),x;C.isIdentityPermutation(A)?x=s[y]:(x=fn({inputs:{x:s[y]},backend:n,attrs:{perm:A}}),f.push(x));let v=x.shape.slice();for(let w=0;w<g.length;++w)v.splice(g[w],0,1);_.arraysEqual(x.shape,v)||(x=Ae({inputs:{x},backend:n,attrs:{shape:v}}),f.push(x)),d===null?d=x:(d=Ty({inputs:{a:x,b:d},backend:n}),f.push(d))}m<h-1&&(c[m]>=0&&(d=Ip({inputs:{x:d},backend:n,attrs:{axis:c[m]-(i.length-p),keepDims:!1}}),f.push(d)),p--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var qj={kernelName:Xh,backendName:"webgl",kernelFunc:Gj},Xj="return (x >= 0.0) ? x : (exp(x) - 1.0);",Kj=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2789,27 +2808,27 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,Ij=Xe({opSnippet:vj,packedOpSnippet:kj}),Nj={kernelName:mo,backendName:"webgl",kernelFunc:Ij},Sj="return (b >= 1.0) ? a : a * (b + 1.0);",Tj=`
+`,Zj=Xe({opSnippet:Xj,packedOpSnippet:Kj}),Yj={kernelName:wo,backendName:"webgl",kernelFunc:Zj},Jj="return (b >= 1.0) ? a : a * (b + 1.0);",Qj=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,Ej=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new gc(Tj,r.shape,a.shape):new Ol(Sj,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},Cj={kernelName:Hh,backendName:"webgl",kernelFunc:Ej},Rj=`
+`,eU=e=>{let{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new vc(Qj,r.shape,a.shape):new Bl(Jj,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)},tU={kernelName:Kh,backendName:"webgl",kernelFunc:eU},nU=`
   return vec4(equal(a, b));
-`,Mj="return float(a == b);",Fj=nn({opSnippet:Mj,packedOpSnippet:Rj,dtype:"bool"}),Dj={kernelName:yo,backendName:"webgl",kernelFunc:Fj},$j=`
+`,rU="return float(a == b);",aU=tn({opSnippet:rU,packedOpSnippet:nU,dtype:"bool"}),sU={kernelName:_o,backendName:"webgl",kernelFunc:aU},iU=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
-  float p = ${R.ERF_P};
-  float a1 = ${R.ERF_A1};
-  float a2 = ${R.ERF_A2};
-  float a3 = ${R.ERF_A3};
-  float a4 = ${R.ERF_A4};
-  float a5 = ${R.ERF_A5};
+  float p = ${C.ERF_P};
+  float a1 = ${C.ERF_A1};
+  float a2 = ${C.ERF_A2};
+  float a3 = ${C.ERF_A3};
+  float a4 = ${C.ERF_A4};
+  float a5 = ${C.ERF_A5};
 
   float sign = sign(x);
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,Oj=Xe({opSnippet:$j}),zj={kernelName:Ao,backendName:"webgl",kernelFunc:Oj},a7="return exp(x);",s7=Xe({opSnippet:a7,packedOpSnippet:a7,cpuKernelImpl:OL}),Pj={kernelName:_s,backendName:"webgl",kernelFunc:s7};function wA(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(v.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),xe({inputs:{x:s},backend:r,attrs:{shape:o}})}var Lj={kernelName:go,backendName:"webgl",kernelFunc:wA},i7="return exp(x) - 1.0;",Wj=Xe({opSnippet:i7,packedOpSnippet:i7,cpuKernelImpl:zL}),Bj={kernelName:xo,backendName:"webgl",kernelFunc:Wj},o7=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,oU=Xe({opSnippet:iU}),lU={kernelName:bo,backendName:"webgl",kernelFunc:oU},w7="return exp(x);",b7=Xe({opSnippet:w7,packedOpSnippet:w7,cpuKernelImpl:sW}),uU={kernelName:ks,backendName:"webgl",kernelFunc:b7};function My(e){let{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(_.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),Ae({inputs:{x:s},backend:r,attrs:{shape:o}})}var cU={kernelName:vo,backendName:"webgl",kernelFunc:My},_7="return exp(x) - 1.0;",hU=Xe({opSnippet:_7,packedOpSnippet:_7,cpuKernelImpl:iW}),dU={kernelName:ko,backendName:"webgl",kernelFunc:hU},v7=class{constructor(e,t,n){this.variableNames=["real","imag"];let r=t[1];this.outputShape=t;let a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2842,13 +2861,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function l7(e,t,n){let r=n.texData.get(e.dataId),a=v.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=xe({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new o7("real",l,t),u=new o7("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=Ga({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=xe({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function Vj(e){let{inputs:t,backend:n}=e,{input:r}=t;return l7(r,!1,n)}var jj={kernelName:Gh,backendName:"webgl",kernelFunc:Vj},Uj=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function k7(e,t,n){let r=n.texData.get(e.dataId),a=_.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=Ae({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),l=o.shape,c=new v7("real",l,t),u=new v7("imag",l,t),h=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:l},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(c,h,"float32"),p=n.runWebGLProgram(u,h,"float32"),f=qa({inputs:{real:d,imag:p},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p);let m=Ae({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function pU(e){let{inputs:t,backend:n}=e,{input:r}=t;return k7(r,!1,n)}var fU={kernelName:Zh,backendName:"webgl",kernelFunc:pU},mU=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       uniform float value;
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function bA(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||v.inferDtype(a),s==="string"){let i=v.getArrayFromDType(s,v.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new Uj(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var Hj={kernelName:vu,backendName:"webgl",kernelFunc:bA},Gj=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Fy(e){let{backend:t,attrs:n}=e,{shape:r,value:a}=n,{dtype:s}=n;if(s=s||_.inferDtype(a),s==="string"){let i=_.getArrayFromDType(s,_.sizeFromShape(r));return i.fill(a),t.makeTensorInfo(r,s,i)}else{let i=new mU(r,a),o=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[],s,o)}}var yU={kernelName:Eu,backendName:"webgl",kernelFunc:Fy},AU=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -2862,7 +2881,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},qj={kernelName:wo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new Gj(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},u7="return floor(x);",Xj=Xe({opSnippet:u7,packedOpSnippet:u7,cpuKernelImpl:PL}),Kj={kernelName:vs,backendName:"webgl",kernelFunc:Xj},Zj=`
+    `}},gU={kernelName:Io,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,r=t,a=new AU(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},I7="return floor(x);",xU=Xe({opSnippet:I7,packedOpSnippet:I7,cpuKernelImpl:oW}),wU={kernelName:Is,backendName:"webgl",kernelFunc:xU},bU=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2872,7 +2891,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
   } else {
     return NAN;
   }
-`,Yj=`
+`,_U=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2893,7 +2912,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,Jj=nn({opSnippet:Zj,packedOpSnippet:Yj,dtype:"int32"}),Qj={kernelName:ks,backendName:"webgl",kernelFunc:Jj},eU=class{constructor(e){this.variableNames=["A"];let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
+`,vU=tn({opSnippet:bU,packedOpSnippet:_U,dtype:"int32"}),kU={kernelName:Ss,backendName:"webgl",kernelFunc:vU},IU=class{constructor(e){this.variableNames=["A"];let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2915,7 +2934,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},tU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
+    `}},SU=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=dn(),[n,r]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2949,7 +2968,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
 
         ${t.output} = result;
       }
-    `}},rU={kernelName:od,backendName:"webgl",kernelFunc:nU},Pl;function nU(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(Pl==null&&(Pl=document.createElement("canvas").getContext("2d")),Pl.canvas.width=l,Pl.canvas.height=c,Pl.drawImage(a,0,0,l,c),a=Pl.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=nr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new tU(h):new eU(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function aU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=R.convertConv2DDataFormat(u),A=R.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=Y3({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=J3({x:a,filter:s,convInfo:A,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,_=o!=null,x=p==="leakyrelu",N=p?xp(p,!1):null,T=new Z3(A,b,N,_,x),E=[a,s];if(i&&E.push(i),o&&E.push(o),x){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push(F),g.push(F)}y=n.runWebGLProgram(T,E,"float32")}let w=xe({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),w}var sU={kernelName:ri,backendName:"webgl",kernelFunc:aU};function iU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),v.assert(R.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let A=R.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=d?xp(d,y):null,w=[a,s],b=i!=null,_=o!=null,x=d==="leakyrelu";if(b&&w.push(i),_&&w.push(o),x){let E=n.makeTensorInfo([],"float32",v.createScalarValue(p,"float32"));w.push(E),f.push(E)}let N;y?N=new r7(A,b,g,_,x):N=new n7(A,b,g,_,x);let T=n.runWebGLProgram(N,w,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),T}var oU={kernelName:ai,backendName:"webgl",kernelFunc:iU},lU=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=ot(t.length),a=ot(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},TU={kernelName:hd,backendName:"webgl",kernelFunc:NU},jl;function NU(e){let{inputs:t,backend:n,attrs:r}=e,{pixels:a}=t,{numChannels:s}=r,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,c]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],u=[c,l],h=[c,l,s];(o||i)&&(jl==null&&(jl=document.createElement("canvas").getContext("2d")),jl.canvas.width=l,jl.canvas.height=c,jl.drawImage(a,0,0,l,c),a=jl.canvas);let d=n.makeTensorInfo(u,"int32");n.texData.get(d.dataId).usage=ar.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),a);let p=J().getBool("WEBGL_PACK")?new SU(h):new IU(h),f=n.runWebGLProgram(p,[d],"int32");return n.disposeData(d.dataId),f}function EU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dataFormat:u,dilations:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=r,m=C.convertConv2DDataFormat(u),y=C.computeConv2DInfo(a.shape,s.shape,l,h,c,d,!1,m),A,g=[];if(y.filterHeight===1&&y.filterWidth===1&&y.dilationHeight===1&&y.dilationWidth===1&&y.strideHeight===1&&y.strideWidth===1&&(y.padInfo.type==="SAME"||y.padInfo.type==="VALID"))A=p7({x:a,filter:s,convInfo:y,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)A=f7({x:a,filter:s,convInfo:y,backend:n,bias:i,activation:p,preluActivationWeights:o,leakyreluAlpha:f});else{let v=i!=null,w=o!=null,b=p==="leakyrelu",k=p?vp(p,!1):null,N=new d7(y,v,k,w,b),E=[a,s];if(i&&E.push(i),o&&E.push(o),b){let F=n.makeTensorInfo([],"float32",_.createScalarValue(f,"float32"));E.push(F),g.push(F)}A=n.runWebGLProgram(N,E,"float32")}let x=Ae({inputs:{x:A},backend:n,attrs:{shape:y.outShape}});return g.push(A),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var CU={kernelName:oi,backendName:"webgl",kernelFunc:EU};function RU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:h,activation:d,leakyreluAlpha:p}=r,f=[],m=u;m==null&&(m=[1,1]),_.assert(C.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let y=C.computeConv2DInfo(a.shape,s.shape,l,m,c,h,!0),A=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels==1,g=d?vp(d,A):null,x=[a,s],v=i!=null,w=o!=null,b=d==="leakyrelu";if(v&&x.push(i),w&&x.push(o),b){let E=n.makeTensorInfo([],"float32",_.createScalarValue(p,"float32"));x.push(E),f.push(E)}let k;A?k=new x7(y,v,g,w,b):k=new g7(y,v,g,w,b);let N=n.runWebGLProgram(k,x,"float32");return f.forEach(E=>n.disposeIntermediateTensorInfo(E)),N}var MU={kernelName:li,backendName:"webgl",kernelFunc:RU},FU=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let r=lt(t.length),a=lt(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${r} strides = ${r}(${this.strides});
          void main() {
           ${a} coords = getOutputCoords();
@@ -2960,21 +2979,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function uU(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=R.prepareAndValidate(r,a),h=xe({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=xe({inputs:{x:r},backend:n,attrs:{shape:[v.sizeFromShape(r.shape)/c,c]}}),p=new lU(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var cU={kernelName:_o,backendName:"webgl",kernelFunc:uU},dU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),r=hU(e,2);this.userCode=`
+      `}};function $U(e){let{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,c,u]=C.prepareAndValidate(r,a),h=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),d=Ae({inputs:{x:r},backend:n,attrs:{shape:[_.sizeFromShape(r.shape)/c,c]}}),p=new FU(i,u,[l,c]),f=n.runWebGLProgram(p,[d,h],d.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(f),m}var DU={kernelName:No,backendName:"webgl",kernelFunc:$U},zU=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=lt(this.rank),r=OU(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function hU(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function pU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=v.parseAxisParam(i,a.shape)[0],c=R.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=v.sizeFromShape(s.shape),h=[],d=xe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=xe({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),w=n.bufferSync(d),b=LL(w,g,f);return h.forEach(_=>n.disposeIntermediateTensorInfo(_)),n.makeTensorInfo(c.outputShape,b.dtype,b.values)}let m=new dU(d.shape,f),A=n.runWebGLProgram(m,[d,p],d.dtype);h.push(A);let y=xe({inputs:{x:A},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var fU={kernelName:bo,backendName:"webgl",kernelFunc:pU},mU="return float(a > b);",AU=`
+    `}};function OU(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let a=0;a<e.length;a++)a===2?r.push("int(getIndices(resRC.x, resRC.z))"):r.push(`${n[a]}`);return r.join()}function PU(e){let{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=r,l=_.parseAxisParam(i,a.shape)[0],c=C.segment_util.collectGatherOpShapeInfo(a,s,l,o),u=_.sizeFromShape(s.shape),h=[],d=Ae({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),p=Ae({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});h.push(d),h.push(p);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let g=n.bufferSync(p),x=n.bufferSync(d),v=lW(x,g,f);return h.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(c.outputShape,v.dtype,v.values)}let m=new zU(d.shape,f),y=n.runWebGLProgram(m,[d,p],d.dtype);h.push(y);let A=Ae({inputs:{x:y},backend:n,attrs:{shape:c.outputShape}});return h.forEach(g=>n.disposeIntermediateTensorInfo(g)),A}var LU={kernelName:So,backendName:"webgl",kernelFunc:PU},WU="return float(a > b);",BU=`
   return vec4(greaterThan(a, b));
-`,yU=nn({opSnippet:mU,packedOpSnippet:AU,cpuKernelImpl:WL,dtype:"bool"}),gU={kernelName:vo,backendName:"webgl",kernelFunc:yU},xU="return float(a >= b);",wU=`
+`,VU=tn({opSnippet:WU,packedOpSnippet:BU,cpuKernelImpl:uW,dtype:"bool"}),jU={kernelName:To,backendName:"webgl",kernelFunc:VU},UU="return float(a >= b);",HU=`
   return vec4(greaterThanEqual(a, b));
-`,bU=nn({opSnippet:xU,packedOpSnippet:wU,dtype:"bool"}),_U={kernelName:Ns,backendName:"webgl",kernelFunc:bU};function vU(e){let{inputs:t,backend:n}=e,{input:r}=t;return l7(r,!0,n)}var kU={kernelName:qh,backendName:"webgl",kernelFunc:vU},IU="return float(!isnan(x) && !isinf(x));",NU=Xe({opSnippet:IU,dtype:"bool"}),SU={kernelName:ko,backendName:"webgl",kernelFunc:NU},TU="return float(isinf(x));",EU=Xe({opSnippet:TU,dtype:"bool"}),CU={kernelName:Io,backendName:"webgl",kernelFunc:EU},RU="return float(isnan(x));",MU=Xe({opSnippet:RU,dtype:"bool"}),FU={kernelName:No,backendName:"webgl",kernelFunc:MU},DU="return float(a < b);",$U=`
+`,GU=tn({opSnippet:UU,packedOpSnippet:HU,dtype:"bool"}),qU={kernelName:Ts,backendName:"webgl",kernelFunc:GU};function XU(e){let{inputs:t,backend:n}=e,{input:r}=t;return k7(r,!0,n)}var KU={kernelName:Yh,backendName:"webgl",kernelFunc:XU},ZU="return float(!isnan(x) && !isinf(x));",YU=Xe({opSnippet:ZU,dtype:"bool"}),JU={kernelName:Eo,backendName:"webgl",kernelFunc:YU},QU="return float(isinf(x));",eH=Xe({opSnippet:QU,dtype:"bool"}),tH={kernelName:Co,backendName:"webgl",kernelFunc:eH},nH="return float(isnan(x));",rH=Xe({opSnippet:nH,dtype:"bool"}),aH={kernelName:Ro,backendName:"webgl",kernelFunc:rH},sH="return float(a < b);",iH=`
   return vec4(lessThan(a, b));
-`,OU=nn({opSnippet:DU,packedOpSnippet:$U,cpuKernelImpl:BL,dtype:"bool"}),zU={kernelName:So,backendName:"webgl",kernelFunc:OU},PU="return float(a <= b);",LU=`
+`,oH=tn({opSnippet:sH,packedOpSnippet:iH,cpuKernelImpl:cW,dtype:"bool"}),lH={kernelName:Mo,backendName:"webgl",kernelFunc:oH},uH="return float(a <= b);",cH=`
   return vec4(lessThanEqual(a, b));
-`,WU=nn({opSnippet:PU,packedOpSnippet:LU,dtype:"bool"}),BU={kernelName:To,backendName:"webgl",kernelFunc:WU};function VU(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=VL(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var jU={kernelName:Kh,backendName:"webgl",kernelFunc:VU},UU=`if (x < 0.0) return NAN;
-  return log(x);`,HU=`
+`,hH=tn({opSnippet:uH,packedOpSnippet:cH,dtype:"bool"}),dH={kernelName:Fo,backendName:"webgl",kernelFunc:hH};function pH(e){let{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=hW(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}var fH={kernelName:Qh,backendName:"webgl",kernelFunc:pH},mH=`if (x < 0.0) return NAN;
+  return log(x);`,yH=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2983,16 +3002,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,GU=Xe({opSnippet:UU,packedOpSnippet:HU,cpuKernelImpl:jL}),qU={kernelName:Es,backendName:"webgl",kernelFunc:GU},XU="return log(1.0 + x);",KU=Xe({opSnippet:XU}),ZU={kernelName:Eo,backendName:"webgl",kernelFunc:KU},YU="return float(a >= 1.0 && b >= 1.0);",JU=`
+`,AH=Xe({opSnippet:mH,packedOpSnippet:yH,cpuKernelImpl:dW}),gH={kernelName:Rs,backendName:"webgl",kernelFunc:AH},xH="return log(1.0 + x);",wH=Xe({opSnippet:xH}),bH={kernelName:$o,backendName:"webgl",kernelFunc:wH},_H="return float(a >= 1.0 && b >= 1.0);",vH=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,QU=nn({opSnippet:YU,packedOpSnippet:JU,dtype:"bool"}),eH={kernelName:Co,backendName:"webgl",kernelFunc:QU},tH="return float(!(x >= 1.0));",nH=Xe({opSnippet:tH}),rH={kernelName:ku,backendName:"webgl",kernelFunc:nH},aH="return float(a >= 1.0 || b >= 1.0);",sH=`
+`,kH=tn({opSnippet:_H,packedOpSnippet:vH,dtype:"bool"}),IH={kernelName:Do,backendName:"webgl",kernelFunc:kH},SH="return float(!(x >= 1.0));",NH=Xe({opSnippet:SH}),TH={kernelName:Cu,backendName:"webgl",kernelFunc:NH},EH="return float(a >= 1.0 || b >= 1.0);",CH=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,iH=nn({opSnippet:aH,packedOpSnippet:sH,dtype:"bool"}),oH={kernelName:Iu,backendName:"webgl",kernelFunc:iH},lH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,RH=tn({opSnippet:EH,packedOpSnippet:CH,dtype:"bool"}),MH={kernelName:Ru,backendName:"webgl",kernelFunc:RH},FH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3011,7 +3030,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},uH=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+    `}},$H=class{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${n}) + float(${r}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3073,7 +3092,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},cH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new uH(a.shape,s,i,o,l):new lH(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},hH={kernelName:Nu,backendName:"webgl",kernelFunc:cH},dH=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
+    `}},DH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,c=J().getBool("WEBGL_PACK_NORMALIZATION")?new $H(a.shape,s,i,o,l):new FH(a.shape,s,i,o,l);return n.runWebGLProgram(c,[a],a.dtype)},OH={kernelName:Mu,backendName:"webgl",kernelFunc:DH},zH=class{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3128,14 +3147,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
       }
       setOutput(result);
       }
-    `}},pH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new dH(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},fH={kernelName:Zh,backendName:"webgl",kernelFunc:pH};function mH(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Si(i,e.dtype,"max",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function c7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,w=new Array(o);for(let x=0;x<w.length;x++)w[x]=a.shape[u[x]];let b=fA(g,a.shape,a.dtype,u,w);p=n.makeTensorInfo(w,a.dtype);let _=n.texData.get(p.dataId);_.values=b}else p=wp(a,u,n);c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("max",c,o);let[f,m]=R.computeOutAndReduceShapes(p.shape,c),A=f;i&&(A=R.expandShapeToKeepDim(f,l));let y;if(d){let g=n.texData.get(p.dataId).values,w=UL(g,v.sizeFromShape(m),A,a.dtype);y=n.makeTensorInfo(A,a.dtype);let b=n.texData.get(y.dataId);b.values=w}else y=mH(p,m,A,n);return h&&n.disposeIntermediateTensorInfo(p),y}var AH={kernelName:Cs,backendName:"webgl",kernelFunc:c7},yH=S3+`
+    `}},PH=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:c,beta:u}=r,h=new zH(a.shape,o,l,c,u);return n.runWebGLProgram(h,[a,s,i],a.dtype)},LH={kernelName:ed,backendName:"webgl",kernelFunc:PH};function WH(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ri(i,e.dtype,"max",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}function S7(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=n.shouldExecuteOnCPU([a]),p=a;if(h){if(d){let g=n.texData.get(p.dataId).values,x=new Array(o);for(let b=0;b<x.length;b++)x[b]=a.shape[u[b]];let v=Sy(g,a.shape,a.dtype,u,x);p=n.makeTensorInfo(x,a.dtype);let w=n.texData.get(p.dataId);w.values=v}else p=kp(a,u,n);c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("max",c,o);let[f,m]=C.computeOutAndReduceShapes(p.shape,c),y=f;i&&(y=C.expandShapeToKeepDim(f,l));let A;if(d){let g=n.texData.get(p.dataId).values,x=pW(g,_.sizeFromShape(m),y,a.dtype);A=n.makeTensorInfo(y,a.dtype);let v=n.texData.get(A.dataId);v.values=x}else A=WH(p,m,y,n);return h&&n.disposeIntermediateTensorInfo(p),A}var BH={kernelName:Ms,backendName:"webgl",kernelFunc:S7},VH=U3+`
   return max(a, b);
-`,gH=`
+`,jH=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+gp+`
+  `+_p+`
   return result;
-`,xH=nn({opSnippet:yH,packedOpSnippet:gH,cpuKernelImpl:HL}),wH={kernelName:Rs,backendName:"webgl",kernelFunc:xH};function bH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Tl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;v.assert(R.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=R.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Wn({inputs:{x:a},backend:n});let h=new xc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var _H={kernelName:Ms,backendName:"webgl",kernelFunc:bH};function vH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=R.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new yA(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var kH={kernelName:Su,backendName:"webgl",kernelFunc:vH},IH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,UH=tn({opSnippet:VH,packedOpSnippet:jH,cpuKernelImpl:fW}),HH={kernelName:Fs,backendName:"webgl",kernelFunc:UH};function GH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Fl(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r,c=1;_.assert(C.eitherStridesOrDilationsAreOne(i,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let u=C.computePool2DInfo(a.shape,s,i,c,o,l);if(u.filterWidth===1&&u.filterHeight===1&&_.arraysEqual(u.inShape,u.outShape))return Vn({inputs:{x:a},backend:n});let h=new kc(u,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}var qH={kernelName:$s,backendName:"webgl",kernelFunc:GH};function XH(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:c}=r,u=[1,1,1],h=C.computePool3DInfo(a.shape,s,i,u,o,c,l),d=new Ey(h,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}var KH={kernelName:Fu,backendName:"webgl",kernelFunc:XH},ZH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3181,7 +3200,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},NH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
+    `}},YH=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=o-1-e.padInfo.front,h=l-1-e.padInfo.top,d=c-1-e.padInfo.left,p=o*l*c-1;this.userCode=`
       const ivec3 pads = ivec3(${u}, ${h}, ${d});
 
       void main() {
@@ -3245,14 +3264,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function SH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=R.computePool3DInfo(i.shape,o,l,h,c,u),p=new yA(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new NH(d),A=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),A}var TH={kernelName:Jh,backendName:"webgl",kernelFunc:SH};function EH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Tl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=R.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new xc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),A=new IH(d),y=n.runWebGLProgram(A,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),y}var CH={kernelName:Yh,backendName:"webgl",kernelFunc:EH};function RH(e,t,n,r){let a=new xc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new xc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var MH={kernelName:Qh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;v.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];v.assert(R.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=R.computePool2DInfo(r.shape,a,s,c,i),[h,d]=RH(r,o,u,l);return[h,d]}};function FH(e,t,n,r){let a=v.sizeFromShape(t),s=v.sizeFromShape(e.shape)/a,i=xe({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Si(i,"float32","mean",r),l=xe({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var DH={kernelName:Fs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=v.parseAxisParam(s,r.shape),c=l,u=R.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let w=i.texData.get(f.dataId).values,b=new Array(o);for(let N=0;N<b.length;N++)b[N]=r.shape[u[N]];let _=fA(w,r.shape,r.dtype,u,b);f=i.makeTensorInfo(b,r.dtype);let x=i.texData.get(f.dataId);x.values=_}else f=wp(r,u,i);p.push(f),c=R.getInnerMostAxes(c.length,o)}R.assertAxesAreInnerMostDims("sum",c,o);let[m,A]=R.computeOutAndReduceShapes(f.shape,c),y=m;a&&(y=R.expandShapeToKeepDim(m,l));let g=FH(f,A,y,i);for(let w of p)i.disposeIntermediateTensorInfo(w);return g}};function $H(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=v.parseAxisParam(s,a.shape),c=l,u=R.getAxesPermutation(c,o),h=a;u!=null&&(h=En({inputs:{x:a},backend:n,attrs:{perm:u}}),c=R.getInnerMostAxes(c.length,a.shape.length)),R.assertAxesAreInnerMostDims("min",c,o);let[d,p]=R.computeOutAndReduceShapes(h.shape,c),f=v.sizeFromShape(p),m=xe({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),A=Si(m,m.dtype,"min",n),y;if(i){let g=R.expandShapeToKeepDim(d,l);y=xe({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=xe({inputs:{x:A},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(A),u!=null&&n.disposeIntermediateTensorInfo(h),y}var OH={kernelName:Ds,backendName:"webgl",kernelFunc:$H},zH=S3+`
+    `}};function JH(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:c,dimRoundingMode:u}=r,h=[1,1,1],d=C.computePool3DInfo(i.shape,o,l,h,c,u),p=new Ey(d,"max",!0),f=n.runWebGLProgram(p,[i],i.dtype),m=new YH(d),y=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),y}var QH={kernelName:nd,backendName:"webgl",kernelFunc:JH};function eG(e){let{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;Fl([s,i],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:h}=r,d=C.computePool2DInfo(o.shape,l,c,1,u,h),p=!0,f=new kc(d,"max",p),m=n.runWebGLProgram(f,[o],o.dtype),y=new ZH(d),A=n.runWebGLProgram(y,[a,m],o.dtype);return n.disposeIntermediateTensorInfo(m),A}var tG={kernelName:td,backendName:"webgl",kernelFunc:eG};function nG(e,t,n,r){let a=new kc(n,"max",!1),s=r.runWebGLProgram(a,[e],"float32");a=new kc(n,"max",!0,!0,t);let i=r.runWebGLProgram(a,[e],"float32");return[s,i]}var rG={kernelName:rd,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;_.assert(r.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);let c=[1,1];_.assert(C.eitherStridesOrDilationsAreOne(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);let u=C.computePool2DInfo(r.shape,a,s,c,i),[h,d]=nG(r,o,u,l);return[h,d]}};function aG(e,t,n,r){let a=_.sizeFromShape(t),s=_.sizeFromShape(e.shape)/a,i=Ae({inputs:{x:e},attrs:{shape:[s,a]},backend:r}),o=Ri(i,"float32","mean",r),l=Ae({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}var sG={kernelName:Ds,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=_.parseAxisParam(s,r.shape),c=l,u=C.getAxesPermutation(c,o),h=u!=null,d=i.shouldExecuteOnCPU([r]),p=[],f=r;if(h){if(d){let x=i.texData.get(f.dataId).values,v=new Array(o);for(let k=0;k<v.length;k++)v[k]=r.shape[u[k]];let w=Sy(x,r.shape,r.dtype,u,v);f=i.makeTensorInfo(v,r.dtype);let b=i.texData.get(f.dataId);b.values=w}else f=kp(r,u,i);p.push(f),c=C.getInnerMostAxes(c.length,o)}C.assertAxesAreInnerMostDims("sum",c,o);let[m,y]=C.computeOutAndReduceShapes(f.shape,c),A=m;a&&(A=C.expandShapeToKeepDim(m,l));let g=aG(f,y,A,i);for(let x of p)i.disposeIntermediateTensorInfo(x);return g}};function iG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=_.parseAxisParam(s,a.shape),c=l,u=C.getAxesPermutation(c,o),h=a;u!=null&&(h=fn({inputs:{x:a},backend:n,attrs:{perm:u}}),c=C.getInnerMostAxes(c.length,a.shape.length)),C.assertAxesAreInnerMostDims("min",c,o);let[d,p]=C.computeOutAndReduceShapes(h.shape,c),f=_.sizeFromShape(p),m=Ae({inputs:{x:h},backend:n,attrs:{shape:[-1,f]}}),y=Ri(m,m.dtype,"min",n),A;if(i){let g=C.expandShapeToKeepDim(d,l);A=Ae({inputs:{x:y},backend:n,attrs:{shape:g}})}else A=Ae({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),u!=null&&n.disposeIntermediateTensorInfo(h),A}var oG={kernelName:Os,backendName:"webgl",kernelFunc:iG},lG=U3+`
   return min(a, b);
-`,PH=`
+`,uG=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+gp+`
+  `+_p+`
   return result;
-`,LH=nn({opSnippet:zH,packedOpSnippet:PH,cpuKernelImpl:GL}),WH={kernelName:$s,backendName:"webgl",kernelFunc:LH},BH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=ot(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
+`,cG=tn({opSnippet:lG,packedOpSnippet:uG,cpuKernelImpl:mW}),hG={kernelName:zs,backendName:"webgl",kernelFunc:cG},dG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let r=e.length,a=lt(r),s=t.map(c=>c[0]).join(","),i=t.map((c,u)=>c[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l=n==="reflect"?0:1;if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3281,7 +3300,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         ${a} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},VH=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=ot(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
+    `}},pG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((p,f)=>p[0]+e[f]+p[1]);let r=e.length,a=lt(r),s=t.map(p=>p[0]).join(","),i=t.map((p,f)=>p[0]+e[f]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=n==="reflect"?0:1,d="";if(r===1){let p=`
         ${a} source = rc;
         if (source < start) {
           source = start * 2 - source - ${h};
@@ -3337,13 +3356,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},jH=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VH(r.shape,a,s):new BH(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},UH={kernelName:Tu,backendName:"webgl",kernelFunc:jH},HH=`if (b == 0.0) return NAN;
-  return mod(a, b);`,GH=`
+    `}},fG=({inputs:e,backend:t,attrs:n})=>{let{x:r}=e,{paddings:a,mode:s}=n,i=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pG(r.shape,a,s):new dG(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)},mG={kernelName:Ps,backendName:"webgl",kernelFunc:fG},yG=`if (b == 0.0) return NAN;
+  return mod(a, b);`,AG=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+gp+`
+  `+_p+`
   return result;
-`,qH=nn({opSnippet:HH,packedOpSnippet:GH}),XH={kernelName:Ro,backendName:"webgl",kernelFunc:qH},KH=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
+`,gG=tn({opSnippet:yG,packedOpSnippet:AG}),xG={kernelName:Oo,backendName:"webgl",kernelFunc:gG},wG=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3365,11 +3384,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zB=Xe({opSnippet:OB}),PB={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},ZH=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},bG=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,YH=`
+return a / b;`,_G=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3387,14 +3406,14 @@ return a / b;`,YH=`
   }
 
   return result;
-`,h7=nn({opSnippet:ZH,packedOpSnippet:YH,checkOutOfBounds:!0}),JH={kernelName:bs,backendName:"webgl",kernelFunc:h7},d7="return a - b;",p7=nn({opSnippet:d7,packedOpSnippet:d7,supportsComplex:!0,cpuKernelImpl:eW}),QH={kernelName:Qs,backendName:"webgl",kernelFunc:p7};function f7(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=v.parseAxisParam([s],a.shape),o=c7({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=R.expandShapeToKeepDim(o.shape,i),c=xe({inputs:{x:o},backend:n,attrs:{shape:l}}),u=p7({inputs:{a,b:c},backend:n}),h=s7({inputs:{x:u},backend:n}),d=AA({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=xe({inputs:{x:d},backend:n,attrs:{shape:l}}),f=h7({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var eG={kernelName:Ys,backendName:"webgl",kernelFunc:f7};function tG(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:f7({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new KH(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var nG={kernelName:ed,backendName:"webgl",kernelFunc:tG},m7="return -x;";function rG(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=XL(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Dl(r.shape,m7):a=new Ha(r.shape,m7),n.runWebGLProgram(a,[r],r.dtype)}var aG={kernelName:Mo,backendName:"webgl",kernelFunc:rG},sG=Hr.nonMaxSuppressionV3Impl;function iG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=sG(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var oG={kernelName:Do,backendName:"webgl",kernelFunc:iG},lG=Hr.nonMaxSuppressionV4Impl;function uG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=lG(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var cG={kernelName:$o,backendName:"webgl",kernelFunc:uG},hG=Hr.nonMaxSuppressionV5Impl;function dG(e){R.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:A,selectedScores:y}=hG(u,h,d,p,f,m);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var pG={kernelName:Oo,backendName:"webgl",kernelFunc:dG},fG=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,N7=tn({opSnippet:bG,packedOpSnippet:_G,checkOutOfBounds:!0}),vG={kernelName:vs,backendName:"webgl",kernelFunc:N7},T7="return a - b;",E7=tn({opSnippet:T7,packedOpSnippet:T7,supportsComplex:!0,cpuKernelImpl:vW}),kG={kernelName:ni,backendName:"webgl",kernelFunc:E7};function C7(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=_.parseAxisParam([s],a.shape),o=S7({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=C.expandShapeToKeepDim(o.shape,i),c=Ae({inputs:{x:o},backend:n,attrs:{shape:l}}),u=E7({inputs:{a,b:c},backend:n}),h=b7({inputs:{x:u},backend:n}),d=Ip({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),p=Ae({inputs:{x:d},backend:n,attrs:{shape:l}}),f=N7({inputs:{a:h,b:p},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),f}var IG={kernelName:ei,backendName:"webgl",kernelFunc:C7};function SG(e){let{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:C7({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),c=l.shape[0],u=l.shape[1],h=new wG(c,u,s),d=h.getCustomSetupFunc(i),p=n.runWebGLProgram(h,[l],"int32",d);return o||n.disposeIntermediateTensorInfo(l),p}var NG={kernelName:ad,backendName:"webgl",kernelFunc:SG},R7="return -x;";function TG(e){let{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){let s=n.texData.get(r.dataId),[i,o]=AW(s.values,r.shape,r.dtype);return n.makeTensorInfo(o,r.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Ll(r.shape,R7):a=new Ga(r.shape,R7),n.runWebGLProgram(a,[r],r.dtype)}var EG={kernelName:zo,backendName:"webgl",kernelFunc:TG},CG=Hr.nonMaxSuppressionV3Impl;function RG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,c=n.readSync(a.dataId),u=n.readSync(s.dataId),{selectedIndices:h}=CG(c,u,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}var MG={kernelName:Lo,backendName:"webgl",kernelFunc:RG},FG=Hr.nonMaxSuppressionV4Impl;function $G(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:d,validOutputs:p}=FG(u,h,i,o,l,c);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([p]))]}var DG={kernelName:Wo,backendName:"webgl",kernelFunc:$G},OG=Hr.nonMaxSuppressionV5Impl;function zG(e){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:c}=r,u=n.readSync(a.dataId),h=n.readSync(s.dataId),d=i,p=o,f=l,m=c,{selectedIndices:y,selectedScores:A}=OG(u,h,d,p,f,m);return[n.makeTensorInfo([y.length],"int32",new Int32Array(y)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var PG={kernelName:Bo,backendName:"webgl",kernelFunc:zG},LG=class{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${r}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},mG=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=v.sizeFromShape(a.shape),c=new fG(l,s,i,o),u=xe({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=xe({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},AG={kernelName:zs,backendName:"webgl",kernelFunc:mG};function Ip(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=bc({inputs:{input:r},backend:n}),s=Ip({inputs:{x:a},backend:n}),i=kp({inputs:{input:r},backend:n}),o=Ip({inputs:{x:i},backend:n}),l=Ga({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return bA({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var yG={kernelName:el,backendName:"webgl",kernelFunc:Ip};function A7(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=bc({inputs:{input:r},backend:n}),s=A7({inputs:{x:a},backend:n}),i=kp({inputs:{input:r},backend:n}),o=Ip({inputs:{x:i},backend:n}),l=Ga({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return bA({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var gG={kernelName:zo,backendName:"webgl",kernelFunc:A7};function xG(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return wA({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=wA({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=K3({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var wG={kernelName:Po,backendName:"webgl",kernelFunc:xG},bG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=ot(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
+    `}},WG=e=>{let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=_.sizeFromShape(a.shape),c=new LG(l,s,i,o),u=Ae({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[u],a.dtype);n.disposeIntermediateTensorInfo(u);let d=[...a.shape,s],p=Ae({inputs:{x:h},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(h),p},BG={kernelName:Ws,backendName:"webgl",kernelFunc:WG};function Cp(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="complex64"){let a=Sc({inputs:{input:r},backend:n}),s=Cp({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Fy({attrs:{shape:r.shape,dtype:r.dtype,value:r.dtype==="string"?"":0},backend:n})}var VG={kernelName:al,backendName:"webgl",kernelFunc:Cp};function M7(e){let{inputs:t,backend:n}=e,{x:r}=t;if(r.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(r.dtype==="complex64"){let a=Sc({inputs:{input:r},backend:n}),s=M7({inputs:{x:a},backend:n}),i=Ep({inputs:{input:r},backend:n}),o=Cp({inputs:{x:i},backend:n}),l=qa({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}else return Fy({attrs:{shape:r.shape,dtype:r.dtype,value:1},backend:n})}var jG={kernelName:Vo,backendName:"webgl",kernelFunc:M7};function UG(e){let{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(t.length===1)return My({inputs:{input:t[0]},backend:n,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(u=>{_.assertShapesMatch(s,u.shape,"All tensors passed to stack must have matching shapes"),_.assert(i===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(u=>{let h=My({inputs:{input:u},backend:n,attrs:{dim:a}});return o.push(h),h}),c=h7({inputs:l,backend:n,attrs:{axis:a}});return o.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var HG={kernelName:jo,backendName:"webgl",kernelFunc:UG},GG=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let r=e.length,a=lt(r),s=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);if(r===1){this.userCode=`
         int start = ${s};
         int end = ${i};
         uniform float value;
@@ -3421,7 +3440,7 @@ return a / b;`,YH=`
           setOutput(getX(${o}));
         }
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},_G=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=ot(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},qG=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let r=e.length,a=lt(r),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=pn("rc",r),l=pn("source",r),c=`${o[r-1]} < ${this.outputShape[r-1]}`,u=r===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;
        if(${c}) {
       `,r===1?"":`}
        rc = outputLoc;
@@ -3446,7 +3465,7 @@ return a / b;`,YH=`
         ${p}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},y7=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _G(a.shape,s,i):new bG(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},vG={kernelName:Ps,backendName:"webgl",kernelFunc:y7},kG=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},F7=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new qG(a.shape,s,i):new GG(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},XG={kernelName:Bs,backendName:"webgl",kernelFunc:F7},KG=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3455,7 +3474,7 @@ return a / b;`,YH=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,IG=`
+`,ZG=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3469,11 +3488,11 @@ return a / b;`,YH=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+gp+`
+  `+_p+`
   return result;
-`,NG=nn({opSnippet:kG,packedOpSnippet:IG}),SG={kernelName:Ls,backendName:"webgl",kernelFunc:NG};function TG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=v.parseAxisParam(s,a.shape),u=c,h=R.getAxesPermutation(u,o),d=a;h!=null&&(d=En({inputs:{x:a},backend:n,attrs:{perm:h}}),u=R.getInnerMostAxes(u.length,o),l.push(d)),R.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:A,outDtype:y}=KL(d.shape,d.dtype,f,u);p=n.makeTensorInfo(A,y,m)}else{let[f,m]=R.computeOutAndReduceShapes(d.shape,u),A=v.sizeFromShape(m),y=xe({inputs:{x:d},backend:n,attrs:{shape:[-1,A]}}),g=dd(a.dtype),w=Si(y,g,"prod",n);p=xe({inputs:{x:w},backend:n,attrs:{shape:f}}),l.push(y),l.push(w)}if(i){l.push(p);let f=R.expandShapeToKeepDim(p.shape,c);p=xe({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var EG={kernelName:Lo,backendName:"webgl",kernelFunc:TG},g7=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=ZL(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},CG={kernelName:Eu,backendName:"webgl",kernelFunc:g7},RG="return 1.0 / x;",MG=Xe({opSnippet:RG}),FG={kernelName:Wo,backendName:"webgl",kernelFunc:MG},DG=Nr+`
+`,YG=tn({opSnippet:KG,packedOpSnippet:ZG}),JG={kernelName:Vs,backendName:"webgl",kernelFunc:YG};function QG(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],c=_.parseAxisParam(s,a.shape),u=c,h=C.getAxesPermutation(u,o),d=a;h!=null&&(d=fn({inputs:{x:a},backend:n,attrs:{perm:h}}),u=C.getInnerMostAxes(u.length,o),l.push(d)),C.assertAxesAreInnerMostDims("prod",u,o);let p;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:y,outDtype:A}=gW(d.shape,d.dtype,f,u);p=n.makeTensorInfo(y,A,m)}else{let[f,m]=C.computeOutAndReduceShapes(d.shape,u),y=_.sizeFromShape(m),A=Ae({inputs:{x:d},backend:n,attrs:{shape:[-1,y]}}),g=yd(a.dtype),x=Ri(A,g,"prod",n);p=Ae({inputs:{x},backend:n,attrs:{shape:f}}),l.push(A),l.push(x)}if(i){l.push(p);let f=C.expandShapeToKeepDim(p.shape,c);p=Ae({inputs:{x:p},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),p}var eq={kernelName:Uo,backendName:"webgl",kernelFunc:QG},$7=e=>{let{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=xW(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},tq={kernelName:$u,backendName:"webgl",kernelFunc:$7},nq="return 1.0 / x;",rq=Xe({opSnippet:nq}),aq={kernelName:Ho,backendName:"webgl",kernelFunc:rq},sq=Sr+`
   return (x < 0.0) ? 0.0 : x;
-`,$G=`
+`,iq=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3483,9 +3502,9 @@ return a / b;`,YH=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,OG=Xe({opSnippet:DG,packedOpSnippet:$G}),zG={kernelName:Bs,backendName:"webgl",kernelFunc:OG},PG=Nr+`
+`,oq=Xe({opSnippet:sq,packedOpSnippet:iq}),lq={kernelName:Us,backendName:"webgl",kernelFunc:oq},uq=Sr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,LG=`
+`,cq=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3495,7 +3514,7 @@ return a / b;`,YH=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,WG=Xe({opSnippet:PG,packedOpSnippet:LG}),BG={kernelName:js,backendName:"webgl",kernelFunc:WG},VG=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,hq=Xe({opSnippet:uq,packedOpSnippet:cq}),dq={kernelName:Gs,backendName:"webgl",kernelFunc:hq},pq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/u[0]},
           ${c[1]/u[1]});
@@ -3528,7 +3547,7 @@ return a / b;`,YH=`
 
         setOutput(newValue);
       }
-    `}},jG=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},fq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${c[0]/u[0]},
           ${c[1]/u[1]},
@@ -3605,7 +3624,7 @@ return a / b;`,YH=`
 
         setOutput(newValue);
       }
-    `}};function UG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new jG(a.shape,l,c,s,i):new VG(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var HG={kernelName:Vs,backendName:"webgl",kernelFunc:UG},GG=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function mq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new fq(a.shape,l,c,s,i):new pq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],"float32")}var yq={kernelName:Hs,backendName:"webgl",kernelFunc:mq},Aq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3686,7 +3705,7 @@ return a / b;`,YH=`
 
         setOutput(accumulator);
       }
-    `}};function qG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new GG(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var XG={kernelName:rd,backendName:"webgl",kernelFunc:qG},KG=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function gq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new Aq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var xq={kernelName:od,backendName:"webgl",kernelFunc:gq},wq=class{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,n,l];let c=[r&&t>1?i-1:i,r&&n>1?o-1:o],u=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0",d;a?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/u[0]},
           ${c[1]/u[1]});
@@ -3708,7 +3727,7 @@ return a / b;`,YH=`
 
         setOutput(newValue);
       }
-    `}};function ZG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new KG(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var YG={kernelName:Cu,backendName:"webgl",kernelFunc:ZG},JG=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function bq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,c]=o,u=new wq(a.shape,l,c,s,i);return n.runWebGLProgram(u,[a],a.dtype)}var _q={kernelName:Du,backendName:"webgl",kernelFunc:bq},vq=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],c=o[0]/l[0],u=o[1]/l[1],h=1/c,d=1/u,p=Math.ceil(h)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3778,17 +3797,17 @@ return a / b;`,YH=`
 
         setOutput(accumulator);
       }
-    `}};function QG(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new JG(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var eq={kernelName:nd,backendName:"webgl",kernelFunc:QG},tq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function kq(e){let{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new vq(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var Iq={kernelName:id,backendName:"webgl",kernelFunc:kq},Sq=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
         }
-      `;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=ot(n);this.userCode=`
+      `;return}let r=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,a=e.map((i,o)=>r(o)).join(","),s=lt(n);this.userCode=`
       void main() {
         ${s} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}},nq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=pn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`
+    `}},Nq=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let r=pn("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=lt(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3816,7 +3835,7 @@ return a / b;`,YH=`
           }
           setOutput(result);
         }
-    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((y,g)=>d(g,p)),m=f.join(","),A=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${A}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function rq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=v.parseAxisParam(s,a.shape);if(i===0)return Wn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new nq(a.shape,o):new tq(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var aq={kernelName:Us,backendName:"webgl",kernelFunc:rq},sq=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
+    `;function o(p){return h(p)}function l(p){return p[n-1]="("+p[n-1]+" + 1)",h(p)}function c(p){return p[n-2]="("+p[n-2]+" + 1)",h(p)}function u(p){return p[n-1]="("+p[n-1]+" + 1)",p[n-2]="("+p[n-2]+" + 1)",h(p)}function h(p){let f=e.map((A,g)=>d(g,p)),m=f.join(","),y=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${y}))`}function d(p,f){return t.indexOf(p)!==-1&&e[p]!==1?`${e[p]} - ${f[p]} - 1`:`${f[p]}`}}};function Tq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=_.parseAxisParam(s,a.shape);if(i===0)return Vn({inputs:{x:a},backend:n});let l=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Nq(a.shape,o):new Sq(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}var Eq={kernelName:qs,backendName:"webgl",kernelFunc:Tq},Cq=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],r=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         uniform vec4 params;
@@ -3836,7 +3855,7 @@ return a / b;`,YH=`
           }
           setOutput(outputValue);
         }
-    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},iq={kernelName:tl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new sq(r.shape,s),[c,u]=R.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},oq=`
+    `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{this.paramsLoc==null&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}},Rq={kernelName:sl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Cq(r.shape,s),[c,u]=C.getImageCenter(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(c,u,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},Mq=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3851,7 +3870,7 @@ return a / b;`,YH=`
       return base + 1.0;
     }
   }
-`,lq=Xe({opSnippet:oq}),uq={kernelName:Hs,backendName:"webgl",kernelFunc:lq},cq="return inversesqrt(x);",hq=Xe({opSnippet:cq,cpuKernelImpl:YL}),dq={kernelName:Gs,backendName:"webgl",kernelFunc:hq},x7=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ot(a.length),l=ot(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
+`,Fq=Xe({opSnippet:Mq}),$q={kernelName:Xs,backendName:"webgl",kernelFunc:Fq},Dq="return inversesqrt(x);",Oq=Xe({opSnippet:Dq,cpuKernelImpl:wW}),zq={kernelName:Ks,backendName:"webgl",kernelFunc:Oq},D7=class{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=lt(a.length),l=lt(s.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,h="";r===1?h="i":r===2&&(h="i, coords[1]");let d=`getUpdates(${h})`,p=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -3871,7 +3890,7 @@ return a / b;`,YH=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function pq(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=R.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=xe({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=xe({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new x7(l,o,p.shape.length,f.shape.length,u,d),y=n.runWebGLProgram(A,[f,p,m],f.dtype),g=xe({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),g}var fq={kernelName:Vo,backendName:"webgl",kernelFunc:pq},mq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=ot(n);this.userCode=`
+      `}};function Pq(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:c,strides:u,outputSize:h}=C.calculateShapes(s,a,i),d=[h/c,c];if(h===0)return n.makeTensorInfo(i,a.dtype);let p=Ae({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=Ae({inputs:{x:s},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),y=new D7(l,o,p.shape.length,f.shape.length,u,d),A=n.runWebGLProgram(y,[f,p,m],f.dtype),g=Ae({inputs:{x:A},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),g}var Lq={kernelName:qo,backendName:"webgl",kernelFunc:Pq},Wq=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let r,a;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)a="resRC",r="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let c=0;c<t.length;c++)l.push(`${i[c]}`),c<e&&o.push(`${i[c]}`);r=o.join(),a=l.join()}let s=lt(n);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${r});
@@ -3881,21 +3900,21 @@ return a / b;`,YH=`
           setOutput(getB(${a}));
         }
       }
-    `}};function Aq(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new mq(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],cr(a.dtype,s.dtype))}var yq={kernelName:jo,backendName:"webgl",kernelFunc:Aq},gq=`
+    `}};function Bq(e){let{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new Wq(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],dr(a.dtype,s.dtype))}var Vq={kernelName:Xo,backendName:"webgl",kernelFunc:Bq},jq=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
-  float scaleAlpha = ${R.SELU_SCALEALPHA};
-  float scale = ${R.SELU_SCALE};
+  float scaleAlpha = ${C.SELU_SCALEALPHA};
+  float scale = ${C.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,xq=Xe({opSnippet:gq}),wq={kernelName:Uo,backendName:"webgl",kernelFunc:xq},bq="return 1.0 / (1.0 + exp(-1.0 * x));",_q=Xe({opSnippet:bq}),vq={kernelName:Xs,backendName:"webgl",kernelFunc:_q},kq=`
+`,Uq=Xe({opSnippet:jq}),Hq={kernelName:Ko,backendName:"webgl",kernelFunc:Uq},Gq="return 1.0 / (1.0 + exp(-1.0 * x));",qq=Xe({opSnippet:Gq}),Xq={kernelName:Ys,backendName:"webgl",kernelFunc:qq},Kq=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,Iq=Xe({opSnippet:kq}),Nq={kernelName:qo,backendName:"webgl",kernelFunc:Iq},Sq=M3+`
+`,Zq=Xe({opSnippet:Kq}),Yq={kernelName:Jo,backendName:"webgl",kernelFunc:Zq},Jq=K3+`
   return sin(x);
-`,Tq=Xe({opSnippet:Sq}),Eq={kernelName:qs,backendName:"webgl",kernelFunc:Tq},Cq=`
+`,Qq=Xe({opSnippet:Jq}),eX={kernelName:Zs,backendName:"webgl",kernelFunc:Qq},tX=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,Rq=Xe({opSnippet:Cq}),Mq={kernelName:Go,backendName:"webgl",kernelFunc:Rq},Fq=`
+`,nX=Xe({opSnippet:tX}),rX={kernelName:Yo,backendName:"webgl",kernelFunc:nX},aX=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3915,9 +3934,9 @@ return a / b;`,YH=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,Dq=Xe({opSnippet:Fq}),$q={kernelName:Xo,backendName:"webgl",kernelFunc:Dq},Oq=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;v.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let c=[],u=y7({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=R.getReshaped(u.shape,s,o,!1),d=R.getPermuted(h.length,s.length,!1),p=R.getReshapedPermuted(u.shape,s,o,!1),f=xe({inputs:{x:u},backend:n,attrs:{shape:h}}),m=En({inputs:{x:f},backend:n,attrs:{perm:d}}),A=xe({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},zq={kernelName:Ru,backendName:"webgl",kernelFunc:Oq};function Pq(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=R.calculateShapes(s,a,o),d=!1,p=new x7(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=xe({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var Lq={kernelName:ad,backendName:"webgl",kernelFunc:Pq};function Wq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=v.parseAxisParam(i,a.shape)[0],l=R.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=wc({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var Bq={kernelName:Ko,backendName:"webgl",kernelFunc:Wq},Vq="return sqrt(x);",jq=Xe({opSnippet:Vq}),Uq={kernelName:Ks,backendName:"webgl",kernelFunc:jq},Hq="return x * x;",Gq=Xe({opSnippet:Hq}),qq={kernelName:Mu,backendName:"webgl",kernelFunc:Gq},w7="return (a - b) * (a - b);",Xq=nn({opSnippet:w7,packedOpSnippet:w7}),Kq={kernelName:Js,backendName:"webgl",kernelFunc:Xq};function Zq({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Nr+`
+`,sX=Xe({opSnippet:aX}),iX={kernelName:Qo,backendName:"webgl",kernelFunc:sX},oX=e=>{let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;_.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,g)=>A*g),l=[[0,0]];l.push(...i);for(let A=1+s.length;A<a.shape.length;++A)l.push([0,0]);let c=[],u=F7({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),h=C.getReshaped(u.shape,s,o,!1),d=C.getPermuted(h.length,s.length,!1),p=C.getReshapedPermuted(u.shape,s,o,!1),f=Ae({inputs:{x:u},backend:n,attrs:{shape:h}}),m=fn({inputs:{x:f},backend:n,attrs:{perm:d}}),y=Ae({inputs:{x:m},backend:n,attrs:{shape:p}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),y},lX={kernelName:Ou,backendName:"webgl",kernelFunc:oX};function uX(e){let{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:c,strides:u,outputSize:h}=C.calculateShapes(s,a,o),d=!1,p=new D7(c,l,a.shape.length,s.shape.length,u,[h,1],d),f=n.runWebGLProgram(p,[s,a,i],s.dtype),m=Ae({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var cX={kernelName:ld,backendName:"webgl",kernelFunc:uX};function hX(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=_.parseAxisParam(i,a.shape)[0],l=C.prepareSplitSize(a,s,o),c=a.shape.length,u=new Array(c).fill(0),h=a.shape.slice();return l.map(d=>{let p=[...h];p[o]=d;let f=Ic({inputs:{x:a},backend:n,attrs:{begin:u,size:p}});return u[o]+=d,f})}var dX={kernelName:el,backendName:"webgl",kernelFunc:hX},pX="return sqrt(x);",fX=Xe({opSnippet:pX}),mX={kernelName:Js,backendName:"webgl",kernelFunc:fX},yX="return x * x;",AX=Xe({opSnippet:yX}),gX={kernelName:zu,backendName:"webgl",kernelFunc:AX},O7="return (a - b) * (a - b);",xX=tn({opSnippet:O7,packedOpSnippet:O7}),wX={kernelName:ti,backendName:"webgl",kernelFunc:xX};function bX({inputs:e,attrs:t,backend:n}){let{x:r}=e,a=Sr+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,s=new Ha(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var Yq={kernelName:Ma,backendName:"webgl",kernelFunc:Zq},Jq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=ot(n.length),s=ot(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
+  `,s=new Ga(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}var _X={kernelName:Fa,backendName:"webgl",kernelFunc:bX},vX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let r=n.length,a=lt(n.length),s=lt(n.length),i="";if(r===1)i="coords * strides + begin";else{let o=0;i=n.map((l,c)=>(o++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${o-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
       ${a} strides = ${a}(${t});
 
@@ -3925,15 +3944,15 @@ return a / b;`,YH=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function Qq(e){let{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:h,shrinkAxisMask:d}=r,{nonStrided:p,$begin:f,$strides:m,size:A,newShape:y,outShape:g}=un.sliceInfo(a.shape,s,i,o,l,c,u,h,d),w=xe({inputs:{x:a},backend:n,attrs:{shape:y}}),b;if(p){let x=wc({inputs:{x:w},backend:n,attrs:{begin:f,size:A}});b=xe({inputs:{x},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(x)}else if(g.some(x=>x===0))b=n.makeTensorInfo(g,a.dtype,[]);else if(n.shouldExecuteOnCPU([w])){let x=n.texData.get(w.dataId).values,N=Be(w.shape,w.dtype,x),T=QL(g,N,m,f);b=n.makeTensorInfo(g,w.dtype,T.values)}else{let x=new Jq(f,m,g);b=n.runWebGLProgram(x,[w],w.dtype)}let _=xe({inputs:{x:b},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(b),_}var eX={kernelName:Zo,backendName:"webgl",kernelFunc:Qq},tX="return tan(x);",nX=Xe({opSnippet:tX}),rX={kernelName:Yo,backendName:"webgl",kernelFunc:nX},aX=`
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@@ -4045,7 +4064,7 @@ return a / b;`,YH=`
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@@ -4151,26 +4170,26 @@ return a / b;`,YH=`
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Please use 'channelsLast'.`);let O=r.makeOutput(p.outShape,"float32"),L=r.dataIdMap.get(O.dataId).id;return Q7(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,y,A,g,x,F,v,w,b,k,N,E,L),O}var XK={kernelName:_s,backendName:"wasm",setupFunc:GK,kernelFunc:qK},KK=!1,ZK=yn(_o,KK,"bool"),YK=mn(ks);function Dy(e){let{inputs:t,attrs:n,backend:r}=e,{input:a}=t,{dim:s}=n,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(_.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),Nr({inputs:{x:a},backend:r,attrs:{shape:o}})}var JK={kernelName:vo,backendName:"wasm",kernelFunc:Dy};function QK(e){let{attrs:{shape:t,value:n,dtype:r},backend:a}=e,s=a.makeOutput(t,r);return a.typedArrayFromHeap(s).fill(n),s}var eZ={kernelName:Eu,backendName:"wasm",kernelFunc:QK},ev;function tZ(e){ev=e.wasm.cwrap(Io,null,["number","number","number","number","number","number"])}function nZ(e){let{inputs:t,backend:n}=e,{image:r}=t,a=n.makeOutput(r.shape,r.dtype),s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,[o,l,c,u]=r.shape;return ev(s,o,l,c,u,i),a}var rZ={kernelName:Io,backendName:"wasm",kernelFunc:nZ,setupFunc:tZ},aZ=mn(Is),sZ=!1,iZ=yn(Ss,sZ),tv;function oZ(e){tv=e.wasm.cwrap(Ns,null,["number","number","number","number","number","number","number"])}function lZ(e){let{backend:t,inputs:n,attrs:r}=e,{varianceEpsilon:a}=r,{x:s,mean:i,variance:o,offset:l,scale:c}=n,u=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(_.sizeFromShape(s.shape)===0)return m;let y=t.dataIdMap.get(m.dataId).id;return tv(u,h,d,p,f,a,y),m}var uZ={kernelName:Ns,backendName:"wasm",setupFunc:oZ,kernelFunc:lZ},nv;function cZ(e){nv=e.wasm.cwrap(oi,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function hZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d),y=Nc[p];if(y==null)throw new Error(`${p} activation not yet supported for FusedConv2D in the wasm backend.`);let A=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,x=m.outChannels,v=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);v=Q.id}let w=m.filterHeight,b=m.filterWidth,k=m.padInfo.top,N=m.padInfo.right,E=m.padInfo.bottom,F=m.padInfo.left,O=m.dilationHeight,L=m.dilationWidth,V=m.strideHeight,j=m.strideWidth,U=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ae=r.makeOutput(m.outShape,"float32"),te=r.dataIdMap.get(ae.dataId).id,ie=o==null?0:r.dataIdMap.get(o.dataId).id;return nv(A,G,ee,Y,g,w,b,v,k,N,E,F,X,O,L,V,j,U,x,y,ie,f||0,te),ae}var dZ={kernelName:oi,backendName:"wasm",setupFunc:cZ,kernelFunc:hZ},rv;function pZ(e){rv=e.wasm.cwrap(li,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fZ(e){let{inputs:t,attrs:n,backend:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:c,dilations:u,dataFormat:h,dimRoundingMode:d,activation:p,leakyreluAlpha:f}=n,m=C.computeConv2DInfo(a.shape,s.shape,l,u,c,d,!0),y=Nc[p];if(y==null)throw new Error(`${p} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=r.dataIdMap.get(a.dataId).id,g=r.dataIdMap.get(s.dataId).id,x=m.outChannels,v=0;if(i!=null){let Q=r.dataIdMap.get(i.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${x})`);v=Q.id}let w=m.filterHeight,b=m.filterWidth,k=m.padInfo.top,N=m.padInfo.right,E=m.padInfo.bottom,F=m.padInfo.left,O=m.dilationHeight,L=m.dilationWidth,V=m.strideHeight,j=m.strideWidth,U=m.inChannels,X=m.padInfo.type==="SAME"?1:0,G=m.batchSize,ee=m.inHeight,Y=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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JZ(e){hv=e.wasm.cwrap(Ps,null,["number","array","number","number","array","array","number","number"])}function QZ(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,mode:a}}=e,s=r.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=r.map(f=>f[0]),h=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(u).buffer),p=new Uint8Array(new Int32Array(h).buffer);return hv(i,c,t.shape.length,Mn[t.dtype],d,p,Oy[a],l),o}var eY={kernelName:Ps,backendName:"wasm",kernelFunc:QZ,setupFunc:JZ},tY=!0,nY=yn(Ls,tY),rY=mn(zo);function zy(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),r=n[0],a=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:r,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var dv;function aY(e){dv=e.wasm.cwrap(Lo,"number",["number","number","number","number","number"])}function 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cY(e){fv=e.wasm.cwrap(Bo,"number",["number","number","number","number","number","number"])}function hY(e){let{backend:t,inputs:n,attrs:r}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=r,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(c.dataId).id,d=fv(u,h,s,a,i,o),{pSelectedIndices:p,selectedSize:f,pSelectedScores:m,pValidOutputs:y}=zy(t,d);t.wasm._free(y);let A=t.makeOutput([f],"int32",p),g=t.makeOutput([f],"float32",m);return[A,g]}var dY={kernelName:Bo,backendName:"wasm",setupFunc:cY,kernelFunc:hY},pY=!1,fY=yn(Po,pY,"bool"),mv;function mY(e){mv=e.wasm.cwrap(Ws,null,["number","number","number","number","number"])}function yY(e){let{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=n.makeOutput([...a.shape,s],"int32"),c=n.dataIdMap.get(l.dataId).id,u=n.dataIdMap.get(a.dataId).id;return mv(u,s,i,o,c),l}var AY={kernelName:Ws,backendName:"wasm",setupFunc:mY,kernelFunc:yY};function 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vY(e){let{inputs:{x:t},backend:n,attrs:{paddings:r,constantValue:a}}=e,s=r.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=r.map(f=>f[0]),h=r.map(f=>f[1]),d=new Uint8Array(new Int32Array(u).buffer),p=new Uint8Array(new Int32Array(h).buffer);return yv(i,c,t.shape.length,Mn[t.dtype],d,p,a,l),o}var kY={kernelName:Bs,backendName:"wasm",kernelFunc:vY,setupFunc:_Y},IY=!1,SY=yn(Vs,IY),Av;function NY(e){Av=e.wasm.cwrap(js,null,["number","number","number"])}function TY(e){let{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=n.dataIdMap.get(r.dataId).id,i=n.dataIdMap.get(a.dataId).id,o=n.makeOutput(r.shape,"float32"),l=n.dataIdMap.get(o.dataId).id;return Av(s,i,l),o}var EY={kernelName:js,backendName:"wasm",setupFunc:NY,kernelFunc:TY},gv;function CY(e){gv=e.wasm.cwrap(Uo,null,["number","number","number","number"])}function 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qt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},Rr=class{constructor(e,t,n,r,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=i,this.id=Kv(),s!=null&&(this.originalName=$v(s),this.name=Ov(this.originalName)),this.rank=t.length}},yne=0,Hp=class{constructor(e,t){this.callArgs=t,this.id=yne++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},gne=0,qe=class extends re.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=gne++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Aa(n)+"_"+Vp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let a=null;e.batchSize!=null&&(a=e.batchSize),n=[a].concat(e.inputShape)}this.batchInputShape=n;let r=e.dtype;r==null&&(r=e.inputDType),r==null&&(r="float32"),this.dtype=r}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new Er(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new B(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Rn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Rn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ma(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Er(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Up(this.weights)}build(e){this.built=!0}getWeights(e=!1){return YA(e?this.trainableWeights:this.weights)}setWeights(e){z(()=>{let t=this.weights;if(t.length!==e.length)throw new B(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(w),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let g=y.sourceLayer,w=y.nodeIndex,b=y.tensorIndex;Xr(w===0,"input layer has >1 nodes"),Xr(b===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(w),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof Vl))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. 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Provided ${n} not understood: ${JSON.stringify(e)}`)}function y6(e,t){return Jne(e,t,"classWeight")}async function g6(e,t,n,r){if(t!=null||r!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let a=z(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());_e(a);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let c=[];for(let p=0;p<this.inputs.length;++p)c.push({key:this.inputs[p],value:n[p]});let u=new Di(c),h=Oc(this.outputs,u,{training:!0}),d;for(let p=0;p<this.lossFunctions.length;++p){let f=this.lossFunctions[p](r[p],h[p]);a[p]!=null&&(f=Qne(f,a[p]));let m=It(f);t.push(m),p===0?d=f:d=se(d,f)}for(let p=0;p<this.metricsTensors.length;++p){let f;if(this.outputs.length>1&&p<this.outputs.length)f=t[p];else{let m=this.metricsTensors[p][0],A=this.metricsTensors[p][1];f=It(m(r[A],h[A]))}Ut(f),s.push(f)}return d=It(d),this.calculateLosses().forEach(p=>{d=se(d,p)}),d},o=this.collectedTrainableWeights.map(c=>c.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>z(()=>{let t=[],n,r=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:r[l]});let i=new Di(s),o=Oc(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=It(c(a[l],o[l]));l===0?n=u:n=se(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],h=It(c(a[u],o[u]));t.push(h)}return t})}async fit(e,t,n={}){return ore(this,e,t,n)}async fitDataset(e,t){return rre(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),r=n[0],a=n[1],s=this.makeTrainFunction()(r.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return _e(s),Rn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,r=n?this.trainableWeights:this.weights,a=this.getWeights(n);for(let s=0;s<r.length;++s)n&&!r[s].trainable||t.push({name:r[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=yd().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-yd().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Aa(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Aa(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let r of t)if(typeof n[r]=="string")e[r]=Aa(n[r]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)a[s]=Ei(e.metrics[s])}this.compile({loss:r,metrics:a,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=kn.getSaveHandlers(e);if(i.length===0)throw new B(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new B(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new B("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await kn.encodeWeights(this.getNamedWeights(t)),r=!1,a=null,s={modelTopology:this.toJSON(a,r),format:dre,generatedBy:`TensorFlow.js tfjs-layers v${iy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await kn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...l),n.data=kn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;p6(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){p6(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ya.className="Model";re.registerClass(ya);var N6=class extends ya{};N6.className="Functional";re.registerClass(N6);async function pre(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let r=$c(n),a=Mr(r,t);if(e.weightsManifest!=null){let s=await kn.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),_e(s)}return a}async function mre(e,t){if(t==null&&(t={}),typeof e=="string"){let n=kn.getLoadHandlers(e,t);if(n.length===0)n.push(kn.browserHTTPRequest(e,t));else if(n.length>1)throw new B(`Found more than one (${n.length}) load handlers for URL 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};xy.className="ThresholdedReLU";re.registerClass(xy);var wy=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new dy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};wy.className="Softmax";re.registerClass(wy);function Gl(e,t,n){if(typeof e=="number")return Ti(e,t);if(e.length!==t)throw new B(`The ${n} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function Fr(e,t,n,r,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+r-1)/r)}function e0(e,t,n,r){if(e==null)return null;if(r==="valid")e=e*t+Ka([n-t,0]);else if(r==="same")e=e*t;else throw new B(`Unsupport padding mode: ${r}.`);return e}function by(e,t){return z(()=>(Et(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function V6(e,t){return z(()=>(Et(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function vre(e,t,n,r=1,a="valid",s,i=1){return z(()=>{if(s==null&&(s=Tr()),Et(s),e.shape.length!==3)throw new B(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new B(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new B(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=bd(e,t,r,a==="same"?"same":"valid","NWC",i);return n!=null&&(o=Zr(o,n)),o})}function j6(e,t,n,r=[1,1],a="valid",s,i,o=null){return z(()=>{if(s==null&&(s=Tr()),Et(s),e.rank!==3&&e.rank!==4)throw new B(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new B(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=by(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ja.conv2d({x:l,filter:t,strides:r,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function kre(e,t,n,r=[1,1,1],a="valid",s,i){return z(()=>{if(s==null&&(s=Tr()),Et(s),e.rank!==4&&e.rank!==5)throw new B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=V6(e,s);if(a==="causal")throw new $e("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=cm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Zr(o,n)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var _y=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",_y.verifyArgs(t),this.rank=e,Gt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new $e(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Gl(t.kernelSize,e,"kernelSize"),this.strides=Gl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,rr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Et(this.dataFormat),this.activation=Ja(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Bt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=Gl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Xr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!CA(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ya(this.activation),useBias:this.useBias,biasInitializer:Nt(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Lc=class extends _y{constructor(e,t){super(e,t);this.kernel=null,Lc.verifyArgs(t),this.filters=t.filters,Gt(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Bt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n,r=this.bias==null?null:this.bias.read(),a=Tv(this.activation.getClassName());if(a!=null&&this.rank===2)n=j6(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)n=vre(e,this.kernel.read(),r,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=j6(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=kre(e,this.kernel.read(),r,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new $e("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=lt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<n.length;++a){let s=Fr(n[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let r=[e[0]];return this.dataFormat==="channelsLast"?(r=r.concat(t),r.push(this.filters)):(r.push(this.filters),r=r.concat(t)),r}getConfig(){let e={filters:this.filters,kernelInitializer:Nt(this.kernelInitializer),kernelRegularizer:ut(this.kernelRegularizer),kernelConstraint:Wt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new B(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Wc=class extends Lc{constructor(e){super(2,e);Wc.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!CA(e.kernelSize,"number",1,2))throw new B(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Wc.className="Conv2D";re.registerClass(Wc);var t0=class extends Lc{constructor(e){super(3,e);t0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new B(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};t0.className="Conv3D";re.registerClass(t0);var vy=class extends Wc{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new B(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=lt(e),e.length!==4)throw new B("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{let n=Pe(e);if(n.shape.length!==4)throw new B(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let r=n.shape,a=r[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=r[s],l=r[i],c=this.kernelSize[0],u=this.kernelSize[1],h=this.strides[0],d=this.strides[1],p=e0(o,h,c,this.padding),f=e0(l,d,u,this.padding),m=[a,p,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let A=_d(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=nt(A,[0,3,1,2])),this.bias!=null&&(A=Zr(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=lt(e);let t=e.slice(),n,r,a;this.dataFormat==="channelsFirst"?(n=1,r=2,a=3):(n=3,r=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=e0(t[r],o,s,this.padding),t[a]=e0(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};vy.className="Conv2DTranspose";re.registerClass(vy);var U6=class extends Lc{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new B("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new B("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Bt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Bt(t.pointwiseConstraint)}build(e){if(e=lt(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new qt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n;if(this.rank===1)throw new $e("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=Tm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Zr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.pointwiseInitializer=Nt(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};U6.className="SeparableConv";var ky=class extends U6{constructor(e){super(2,e)}};ky.className="SeparableConv2D";re.registerClass(ky);var n0=class extends Lc{constructor(e){super(1,e);n0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!CA(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};n0.className="Conv1D";re.registerClass(n0);var Iy=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=Mp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Mp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Mp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Mp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="Cropping2D";re.registerClass(Iy);var Ny=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Et(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Hee(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="UpSampling2D";re.registerClass(Ny);function Ire(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Tr()),Et(a);let i=by(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=fl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var Sy=class extends _y{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Bt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=lt(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n=Ire(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Zr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=lt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=Fr(t,this.kernelSize[0],this.padding,this.strides[0]),s=Fr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Nt(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};Sy.className="DepthwiseConv2D";re.registerClass(Sy);function H6(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function G6(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Cr(2,l));if(t=nt(t,c),s!=null)throw new $e("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Qt(a,-1)),a=nt(a,c)),r&&(t=Pn(t,0),a!=null&&(a=Pn(a,0)));let u=[],h,d=n,p=t.shape[0],f=pr(t),m;a!=null&&(m=pr(a));for(let y=0;y<p;++y){let g=f[y],w=z(()=>e(g,d));if(a==null)h=w[0],d=w[1];else{let b=z(()=>{let _=m[y],x=zn(_).sub(_),N=w[0].mul(_).add(d[0].mul(x)),T=d.map((E,F)=>w[1][F].mul(_).add(E.mul(x)));return{output:N,newStates:T}});h=b.output,d=b.newStates}o&&u.push(h)}let A;return o&&(A=cn(u,1)),[h,A,d]})}var Yr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new r0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new qt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Cr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ZA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new $e("Constants support is not implemented in RNN yet.");ZA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new qt({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new $e("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new qt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ma("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_=[Ct([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Ct([n,r])):this.states_[0]=Ct([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!v.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>Ut(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=H6(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Rr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=G6((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return z(()=>{let t=Ct(e.shape);return t=Te(t,[1,2]),t=Cc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?OA(t,[1,n]):t):this.cell.stateSize>1?[OA(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Yr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Mr(r,n);return new e(Object.assign(t,{cell:a}))}};Yr.className="RNN";re.registerClass(Yr);var Fc=class extends qe{},a0=class extends Fc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Gt(this.units,"units"),this.activation=Ja(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Bl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ka([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>zn(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>zn(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Kr(P(e,s),this.kernel.read()):a=Kr(e,this.kernel.read()),this.bias!=null&&(a=Zr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Kr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ya(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};a0.className="SimpleRNNCell";re.registerClass(a0);var Ty=class extends Yr{constructor(e){e.cell=new a0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};Ty.className="SimpleRNN";re.registerClass(Ty);var s0=class extends Fc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Gt(this.units,"units"),this.activation=Ja(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ja(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Bl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ka([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=lt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>zn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>zn(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let c=Kr(e,this.kernel.read());this.useBias&&(c=Zr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Pt(u,[2*this.units,this.units],u.rank-1),p=Kr(r,h),[f,m,A]=Pt(c,3,c.rank-1),[y,g]=Pt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,y)),o=this.recurrentActivation.apply(se(m,g));let w=Kr(P(o,r),d);l=this.activation.apply(se(A,w));let b=se(P(i,r),P(se(1,kt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ya(this.activation),recurrentActivation:Ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};s0.className="GRUCell";re.registerClass(s0);var Ey=class extends Yr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new s0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Ey.className="GRU";re.registerClass(Ey);var Bc=class extends Fc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Gt(this.units,"units"),this.activation=Ja(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ja(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Bl([1,Ka([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Bl([1,Ka([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=lt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends Ar{apply(i,o){let l=a.apply([s]),c=new Dp().apply([s]),u=a.apply([s*2]);return Pv(Pv(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>zn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>zn(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let h=Kr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,i[0])),h=se(h,Kr(r,this.recurrentKernel.read())),this.useBias&&(h=Zr(h,this.bias.read()));let[d,p,f,m]=Pt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(P(l,a),P(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let A=P(u,this.activation.apply(c));return[A,A,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ya(this.activation),recurrentActivation:Ya(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),recurrentInitializer:Nt(this.recurrentInitializer),biasInitializer:Nt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Bc.className="LSTMCell";re.registerClass(Bc);var Cy=class extends Yr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Bc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Cy.className="LSTM";re.registerClass(Cy);var r0=class extends Fc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){ZA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Ri(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(Mr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return YA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}JA(t)}};r0.className="StackedRNNCells";re.registerClass(r0);function Qa(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>Wv(t(),n),i=()=>Mc(s,t,r);return!a||a<=1?Ut(i().clone()):Array(a).fill(void 0).map(i).map(o=>Ut(o.clone()))}var Nre=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},q6=class extends Yr{constructor(e){if(e.unroll)throw new $e("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new $e("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new qt({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Ct(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ma("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_=[Ct(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ct(a)):this.states_[0]=Ct(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!v.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Ut(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=Fr(l,r[0],a,s[0],i[0]),h=Fr(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};q6.className="ConvRNN2D";var i0=class extends Bc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Gt(this.filters,"filters"),this.kernelSize=Gl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Gt(o,"kernelSize")),this.strides=Gl(r||1,2,"strides"),this.strides.forEach(o=>Gt(o,"strides")),this.padding=a||"valid",rr(this.padding),this.dataFormat=s||"channelsLast",Et(this.dataFormat),this.dilationRate=Gl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Gt(o,"dilationRate"))}build(e){var t;e=lt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends Ar{apply(u,h){let d=l.apply([c]),p=jr([c]),f=l.apply([c*2]);return PA([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Qa({ones:()=>zn(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Qa({ones:()=>zn(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),A=l(a,p,2),y=l(a,p,3),g=3,[w,b,_,x]=Pt(this.kernel.read(),i,g),[N,T,E,F]=this.useBias?Pt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,w,N,this.padding),u=this.inputConv(u,b,T,this.padding),h=this.inputConv(h,_,E,this.padding),d=this.inputConv(d,x,F,this.padding);let[$,L,V,j]=Pt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,$),m=this.recurrentConv(m,L),A=this.recurrentConv(A,V),y=this.recurrentConv(y,j);let U=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(P(X,s),P(U,this.activation.apply(se(h,A)))),ee=P(this.recurrentActivation.apply(se(d,y)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Nre(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=ua(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Zr(a,n,this.dataFormat):a}recurrentConv(e,t){return ua(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};i0.className="ConvLSTM2DCell";re.registerClass(i0);var Ry=class extends q6{constructor(e){let t=new i0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Ry.className="ConvLSTM2D";re.registerClass(Ry);var o0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return Mc(()=>Wv(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};o0.className="Dropout";re.registerClass(o0);var My=class extends o0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};My.className="SpatialDropout1D";re.registerClass(My);var Fy=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Gt(this.units,"units"),this.activation=Ja(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Bt(e.kernelConstraint),this.biasConstraint=Bt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=lt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=lt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=Tv(this.activation.getClassName()),a;return r!=null?a=Kr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Kr(n,this.kernel.read()),this.bias!=null&&(a=Zr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ya(this.activation),useBias:this.useBias,kernelInitializer:Nt(this.kernelInitializer),biasInitializer:Nt(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Fy.className="Dense";re.registerClass(Fy);var Dy=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=lt(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Xa(e,1)]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let r=[0];for(let a=2;a<n.rank;++a)r.push(a);r.push(1),n=n.transpose(r)}return Yee(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Dy.className="Flatten";re.registerClass(Dy);var $y=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ja(e.activation)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:Ya(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};$y.className="Activation";re.registerClass($y);var Oy=class extends qe{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return z(()=>(e=Pe(e),Kee(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="RepeatVector";re.registerClass(Oy);var zy=class extends qe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",r=t.slice(),a=1,s=null;for(let o=0;o<r.length;++o){let l=r[o];if(this.isUnknown(l))if(s===null)s=o;else throw new B("Can only specifiy one unknown dimension.");else a*=l}let i=Xa(e);if(s!==null){if(a===0||i%a!=0)throw new B(n);r[s]=i/a}else if(i!==a)throw new B(n);return r}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};zy.className="Reshape";re.registerClass(zy);var Py=class extends qe{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Cr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=lt(e);let t=e.slice();return this.dims.forEach((n,r)=>{t[r+1]=e[n]}),t}call(e,t){return nt(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Py.className="Permute";re.registerClass(Py);var Ly=class extends qe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),r=-1;return Hu(yi(n,this.maskValue),r)}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=-1,a=!0,s=Hu(yi(n,this.maskValue),r,a);return n.mul(s.asType(n.dtype))})}};Ly.className="Masking";re.registerClass(Ly);var Wy=class extends qe{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(ft(e.inputLength))}this.inputDim=e.inputDim,Gt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Gt(this.outputDim,"outputDim"),this.embeddingsInitializer=gt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=xt(e.embeddingsRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.embeddingsConstraint=Bt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return z(()=>this.maskZero?(e=Pe(e),yi(e,Ue(e))):null)}computeOutputShape(e){if(e=lt(e),this.inputLength==null)return[...e,this.outputDim];let t=ft(this.inputLength);if(t.length!==e.length-1)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r<t.length;++r){let a=t[r],s=e[r+1];if(a!=null&&s!=null&&a!==s)throw new B(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=Ec(n,"int32")),Lv(this.embeddings.read(),n.as1D()).reshape(lt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Nt(this.embeddingsInitializer),embeddingsRegularizer:ut(this.embeddingsRegularizer),activityRegularizer:ut(this.activityRegularizer),embeddingsConstraint:Wt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Wy.className="Embedding";re.registerClass(Wy);var Oi=class extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new $e}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[lt(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Gy.className="Concatenate";re.registerClass(Gy);function Vc(e,t){for(;e<0;)e+=t;return e}function Sre(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new $e("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new $e("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var qy=class extends Oi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new $e("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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Xy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return Mc(()=>Fp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Xy.className="GaussianNoise";re.registerClass(Xy);var Ky=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?Mc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Fp(n.shape,1,r))},()=>n,t.training||!1):n})}};Ky.className="GaussianDropout";re.registerClass(Ky);var Zy=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=Cr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Ti(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!v.arraysEqual(c,Cr(0,s).slice(0,s-1)),h=()=>{if(u){let A=this.movingMean.read().reshape(l),y=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,w=this.scale?this.gamma.read().reshape(l):null;return jc(r,A,y,g,w,this.epsilon)}else return 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s==="max"?i=ec(e,t,n,o):i=qu(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function X6(e,t,n,r,a,s){return z(()=>{Et(a),Mv(s),rr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Tr()),s==null&&(s="max"),e=V6(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=bm(e,t,n,o):i=om(e,t,n,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var K6=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Gt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=Fr(t,this.poolSize[0],this.padding,this.strides[0]),n=Fr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},n2=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),rr(r),l0(e,t,n,r,a,"max")}};n2.className="MaxPooling2D";re.registerClass(n2);var r2=class extends Z6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),rr(r),l0(e,t,n,r,a,"avg")}};r2.className="AveragePooling2D";re.registerClass(r2);var Y6=class extends 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t=Fr(t,this.poolSize[0],this.padding,this.strides[0]),n=Fr(n,this.poolSize[1],this.padding,this.strides[1]),r=Fr(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},a2=class extends Y6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),rr(r),X6(e,t,n,r,a,"max")}};a2.className="MaxPooling3D";re.registerClass(a2);var s2=class extends Y6{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Et(a),rr(r),X6(e,t,n,r,a,"avg")}};s2.className="AveragePooling3D";re.registerClass(s2);var J6=class extends qe{constructor(e){super(e);this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Mr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Fre:e.mergeMode,Mre(this.mergeMode),e.weights)throw new $e("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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a=D6(e[0],t);n=a.sorted,r=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=D6(s,t);for(let l of i)a.has(l.name)||(n.push(l),a.add(l.name));for(let l in o)r[l]==null&&(r[l]=new Set),o[l].forEach(c=>r[l].add(c))}}return{sorted:n,recipientCounts:Fre(r)}}function Fre(e){let t={};for(let n in e)t[n]=e[n].size;return t}function D6(e,t){let n=new Set,r=[],a={};for(let o of t.names())n.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(n.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),r.push(o),n.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let c of o.inputs)a[c.name]==null&&(a[c.name]=new Set),a[c.name].add(o.name),!n.has(c.name)&&s.push(c)}}return{sorted:r,recipientMap:a}}function Mre(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let r=0;r<e.sourceLayer.inboundNodes.length;++r)for(let a of 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e={alphaInitializer:St(this.alphaInitializer),alphaRegularizer:ut(this.alphaRegularizer),alphaConstraint:Wt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};CA.className="PReLU";re.registerClass(CA);var RA=class extends qe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new De(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return wl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};RA.className="ELU";re.registerClass(RA);var MA=class extends qe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Pe(e);return n.mul($c(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let 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B(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new B(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=o4(e,s);if(a==="causal")throw new De("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=bm(o,t,r,a==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Rr(o,n)),s==="channelsFirst"&&(o=Je(o,[0,4,1,2,3])),o})}var DA=class extends qe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",DA.verifyArgs(t),this.rank=e,Ht(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new De(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Zl(t.kernelSize,e,"kernelSize"),this.strides=Zl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,sr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=es(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Bt(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=Zl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new B(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new B(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new B(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Xr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!jy(e.kernelSize,"number",1,3))throw new B(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Qa(this.activation),useBias:this.useBias,biasInitializer:St(this.biasInitializer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Uc=class extends DA{constructor(e,t){super(e,t);this.kernel=null,Uc.verifyArgs(t),this.filters=t.filters,Ht(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Bt(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new B(`The channel dimension of the input should be defined. 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Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new B(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Bt(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Bt(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new B(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],r=this.kernelSize.concat([n,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",r,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Ft({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n;if(this.rank===1)throw new De("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Je(e,[0,2,3,1])),n=Wm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Rr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Je(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=St(this.depthwiseInitializer),e.pointwiseInitializer=St(this.pointwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.pointwiseRegularizer=ut(this.pointwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseConstraint),e.pointwiseConstraint=Wt(this.pointwiseConstraint),e}};u4.className="SeparableConv";var PA=class extends u4{constructor(e){super(2,e)}};PA.className="SeparableConv2D";re.registerClass(PA);var s0=class extends Uc{constructor(e){super(1,e);s0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!jy(e.kernelSize,"number",1,1))throw new B(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};s0.className="Conv1D";re.registerClass(s0);var LA=class extends qe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return z(()=>{if(e=Pe(e),this.dataFormat==="channelsLast"){let n=zp(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return zp(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=zp(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return zp(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};LA.className="Cropping2D";re.registerClass(LA);var WA=class extends qe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Ste(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return z(()=>{let n=Pe(e),r=n.shape;if(this.dataFormat==="channelsFirst"){n=Je(n,[0,2,3,1]);let a=this.size[0]*r[2],s=this.size[1]*r[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s]);return Je(i,[0,3,1,2])}else{let a=this.size[0]*r[1],s=this.size[1]*r[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([a,s]):n.resizeBilinear([a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};WA.className="UpSampling2D";re.registerClass(WA);function oae(e,t,n=[1,1],r="valid",a,s){return z(()=>{a==null&&(a=Tr()),Ct(a);let i=$A(e,a);if(e.rank!==4)throw new B(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new B(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=xl(i,t,n,r==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}var BA=class extends DA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Bt(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new B(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new B(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{e=Pe(e);let n=oae(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Rr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=$r(t,this.kernelSize[0],this.padding,this.strides[0]),s=$r(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=St(this.depthwiseInitializer),e.depthwiseRegularizer=ut(this.depthwiseRegularizer),e.depthwiseConstraint=Wt(this.depthwiseRegularizer),e}};BA.className="DepthwiseConv2D";re.registerClass(BA);function c4(e,t,n,r){if(Array.isArray(e)){if(t!=null||n!=null)throw new B("When inputs is an array, neither initialState or constants should be provided");r!=null&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),n=a(n),{inputs:e,initialState:t,constants:n}}function h4(e,t,n,r=!1,a,s,i=!1,o=!1){return z(()=>{let l=t.shape.length;if(l<3)throw new B(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(Cr(2,l));if(t=Je(t,c),s!=null)throw new De("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=a.asType("bool").asType("float32"),a.rank===l-1&&(a=Jt(a,-1)),a=Je(a,c)),r&&(t=Wn(t,0),a!=null&&(a=Wn(a,0)));let u=[],h,d=n,p=t.shape[0],f=mr(t),m;a!=null&&(m=mr(a));for(let A=0;A<p;++A){let g=f[A],x=z(()=>e(g,d));if(a==null)h=x[0],d=x[1];else{let v=z(()=>{let w=m[A],b=Ln(w).sub(w),k=x[0].mul(w).add(d[0].mul(b)),N=d.map((E,F)=>x[1][F].mul(w).add(E.mul(b)));return{output:k,newStates:N}});h=v.output,d=v.newStates}o&&u.push(h)}let y;return o&&(y=cn(u,1)),[h,y,d]})}var Zr=class extends qe{constructor(e){super(e);let t;if(e.cell==null)throw new B("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new i0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new B("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ft({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Cr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){uA(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],r;if(this.returnSequences?r=[e[0],e[1],n]:r=[e[0],n],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[r].concat(a)}else return r}computeMask(e,t){return z(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let r=this.states.map(a=>null);return[n].concat(r)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new De("Constants support is not implemented in RNN yet.");uA(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Ft({shape:[n,null,...r]});let a=[e[0]].concat(e.slice(2));if(t!=null)throw new De("Constants support is not implemented in RNN yet.");this.cell.build(a);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!_.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new B(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ft({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ya("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Rt([n,r])):this.states_=[Rt([n,this.cell.stateSize])];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(r=>Rt([n,r])):this.states_[0]=Rt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let r=0;r<this.states_.length;++r){let a=e[r],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[r]:this.cell.stateSize,i=[n,s];if(!_.arraysEqual(a.shape,i))throw new B(`State ${r} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[r]=a}}this.states_=this.states_.map(r=>jt(r.clone()))})}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=c4(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ft({shape:o.shape}));i=i.concat(this.stateSpec)}if(r!=null&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof Mr){let o=[e].concat(s),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let u=super.apply(o,t);return this.inputSpec=c,u}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;e=Pe(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new B(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:r},o=h4((d,p)=>{let f=this.cell.call([d].concat(p),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],c=o[1],u=o[2];this.stateful&&this.resetStates(u,r);let h=this.returnSequences?c:l;return this.returnState?[h].concat(u):h})}getInitialState(e){return z(()=>{let t=Rt(e.shape);return t=Te(t,[1,2]),t=Dc(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ky(t,[1,n]):t):this.cell.stateSize>1?[Ky(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let r=t.cell,a=Fr(r,n);return new e(Object.assign(t,{cell:a}))}};Zr.className="RNN";re.registerClass(Zr);var Pc=class extends qe{},o0=class extends Pc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let r=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:r})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(n),rate:this.recurrentDropout,training:r}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Kr(P(e,s),this.kernel.read()):a=Kr(e,this.kernel.read()),this.bias!=null&&(a=Rr(a,this.bias.read())),i!=null&&(n=P(n,i));let o=se(a,Kr(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qa(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};o0.className="SimpleRNNCell";re.registerClass(o0);var VA=class extends Zr{constructor(e){e.cell=new o0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return new e(t)}};VA.className="SimpleRNN";re.registerClass(VA);var l0=class extends Pc{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new B("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return z(()=>{if(e=e,e.length!==2)throw new B(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,r=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(r),rate:this.recurrentDropout,training:n,count:3}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let c=Kr(e,this.kernel.read());this.useBias&&(c=Rr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,s[0]));let u=this.recurrentKernel.read(),[h,d]=Pt(u,[2*this.units,this.units],u.rank-1),p=Kr(r,h),[f,m,y]=Pt(c,3,c.rank-1),[A,g]=Pt(p,2,p.rank-1);i=this.recurrentActivation.apply(se(f,A)),o=this.recurrentActivation.apply(se(m,g));let x=Kr(P(o,r),d);l=this.activation.apply(se(y,x));let v=se(P(i,r),P(se(1,kt(i)),l));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qa(this.activation),recurrentActivation:Qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};l0.className="GRUCell";re.registerClass(l0);var jA=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new l0(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};jA.className="GRU";re.registerClass(jA);var qc=class extends Pc{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Bt(e.kernelConstraint),this.recurrentConstraint=Bt(e.recurrentConstraint),this.biasConstraint=Bt(e.biasConstraint),this.dropout=Hl([1,Ya([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Hl([1,Ya([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let r;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;r=new(t=class extends Ar{apply(i,o){let l=a.apply([s]),c=new Lp().apply([s]),u=a.apply([s*2]);return r6(r6(l,c),u)}},t.className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new B(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(r),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,c,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let h=Kr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(r=P(r,i[0])),h=se(h,Kr(r,this.recurrentKernel.read())),this.useBias&&(h=Rr(h,this.bias.read()));let[d,p,f,m]=Pt(h,4,h.rank-1);o=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(p),c=se(P(l,a),P(o,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let y=P(u,this.activation.apply(c));return[y,y,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Qa(this.activation),recurrentActivation:Qa(this.recurrentActivation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),recurrentInitializer:St(this.recurrentInitializer),biasInitializer:St(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:ut(this.kernelRegularizer),recurrentRegularizer:ut(this.recurrentRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),recurrentConstraint:Wt(this.recurrentConstraint),biasConstraint:Wt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};qc.className="LSTMCell";re.registerClass(qc);var UA=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new qc(e),super(e)}call(e,t){return z(()=>{this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};UA.className="LSTM";re.registerClass(UA);var i0=class extends Pc{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return z(()=>{e=e;let n=e.slice(1),r=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?r.push(n.splice(0,i.stateSize.length)):r.push(n.splice(0,1));r.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=r[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),a.push(s.slice(1))}n=[];for(let i of a.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){uA(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,r)=>{Di(`RNNCell_${r}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let r=[];for(let a of t.cells)r.push(Fr(a,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return cA(e)}setWeights(e){let t=[];for(let n of this.cells){let r=n.weights.length,a=e.splice(r);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],a[s]])}hA(t)}};i0.className="StackedRNNCells";re.registerClass(i0);function ts(e){let{ones:t,rate:n,training:r=!1,count:a=1}=e,s=()=>s6(t(),n),i=()=>zc(s,t,r);return!a||a<=1?jt(i().clone()):Array(a).fill(void 0).map(i).map(o=>jt(o.clone()))}var lae=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var a=0,r=Object.getOwnPropertySymbols(e);a<r.length;a++)t.indexOf(r[a])<0&&Object.prototype.propertyIsEnumerable.call(e,r[a])&&(n[r[a]]=e[r[a]]);return n},d4=class extends Zr{constructor(e){if(e.unroll)throw new De("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new De("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ft({ndim:5})]}call(e,t){return z(()=>{if(this.cell.dropoutMask!=null&&(_e(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(_e(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new B("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,r=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return z(()=>{let{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)],s=Rt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){z(()=>{if(!this.stateful)throw new ya("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(n[0]==null)throw new B("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(a)):this.states_=[Rt(a)];else if(e==null)_e(this.states_),this.keptStates!=null&&(_e(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Rt(a)):this.states_[0]=Rt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new B(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):_e(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!_.arraysEqual(i.shape,o))throw new B(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>jt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],c=e[o?4:3],u=$r(l,r[0],a,s[0],i[0]),h=$r(c,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,u,h]:[u,h,n]]}};d4.className="ConvRNN2D";var u0=class extends qc{constructor(e){let{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ht(this.filters,"filters"),this.kernelSize=Zl(n,2,"kernelSize"),this.kernelSize.forEach(o=>Ht(o,"kernelSize")),this.strides=Zl(r||1,2,"strides"),this.strides.forEach(o=>Ht(o,"strides")),this.padding=a||"valid",sr(this.padding),this.dataFormat=s||"channelsLast",Ct(this.dataFormat),this.dilationRate=Zl(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Ht(o,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new B(`The channel dimension of the input should be defined. Found ${e[n]}`);let r=e[n],a=4,s=this.kernelSize.concat([r,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;o=new(t=class extends Ar{apply(u,h){let d=l.apply([c]),p=Pn([c]),f=l.apply([c*2]);return Yy([d,p,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return z(()=>{if(e.length!==3)throw new B(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,r=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ts({ones:()=>Ln(r),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,l=(Y,ae,te)=>!ae||!ae[te]?Y:P(ae[te],Y),c=l(r,o,0),u=l(r,o,1),h=l(r,o,2),d=l(r,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ts({ones:()=>Ln(a),rate:this.recurrentDropout,training:n,count:i}));let p=this.recurrentDropoutMask,f=l(a,p,0),m=l(a,p,1),y=l(a,p,2),A=l(a,p,3),g=3,[x,v,w,b]=Pt(this.kernel.read(),i,g),[k,N,E,F]=this.useBias?Pt(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,x,k,this.padding),u=this.inputConv(u,v,N,this.padding),h=this.inputConv(h,w,E,this.padding),d=this.inputConv(d,b,F,this.padding);let[O,L,V,j]=Pt(this.recurrentKernel.read(),i,g);f=this.recurrentConv(f,O),m=this.recurrentConv(m,L),y=this.recurrentConv(y,V),A=this.recurrentConv(A,j);let U=this.recurrentActivation.apply(se(c,f)),X=this.recurrentActivation.apply(se(u,m)),G=se(P(X,s),P(U,this.activation.apply(se(h,y)))),ee=P(this.recurrentActivation.apply(se(d,A)),this.activation.apply(G));return[ee,ee,G]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=lae(e,["units"]),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,r)}inputConv(e,t,n,r){let a=ca(e,t,this.strides,r||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Rr(a,n,this.dataFormat):a}recurrentConv(e,t){return ca(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};u0.className="ConvLSTM2DCell";re.registerClass(u0);var HA=class extends d4{constructor(e){let t=new u0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};HA.className="ConvLSTM2D";re.registerClass(HA);var c0=class extends qe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let r=0;r<this.noiseShape.length;++r)n.push(this.noiseShape[r]==null?t[r]:this.noiseShape[r]);return n}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let r=t.training==null?!1:t.training,a=this.getNoiseShape(n);return zc(()=>s6(n,this.rate,a,this.seed),()=>n,r)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};c0.className="Dropout";re.registerClass(c0);var GA=class extends c0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};GA.className="SpatialDropout1D";re.registerClass(GA);var qA=class extends qe{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ht(this.units,"units"),this.activation=es(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Bt(e.kernelConstraint),this.biasConstraint=Bt(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e),r=qv(this.activation.getClassName()),a;return r!=null?a=Kr(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=Kr(n,this.kernel.read()),this.bias!=null&&(a=Rr(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Qa(this.activation),useBias:this.useBias,kernelInitializer:St(this.kernelInitializer),biasInitializer:St(this.biasInitializer),kernelRegularizer:ut(this.kernelRegularizer),biasRegularizer:ut(this.biasRegularizer),activityRegularizer:ut(this.activityRegularizer),kernelConstraint:Wt(this.kernelConstraint),biasConstraint:Wt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Dense";re.registerClass(qA);var XA=class extends qe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new B(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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extends qe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new De}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let r=0;r<t.length;++r){let a=e[e.length-t.length+r],s=t[r];if(a==null||s==null||a<0||s<0)n.push(null);else if(a===1)n.push(s);else if(s===1)n.push(a);else{if(a!==s)throw new B("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(a)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[st(e)]),e=e,e.length<2)throw new B(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};i2.className="Concatenate";re.registerClass(i2);function Xc(e,t){for(;e<0;)e+=t;return e}function uae(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new De("batchDot is not implemented for tensors of 4D or higher rank yet");if(_.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),_.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new De("batchDot is not implemented for complex64-type Tensors yet.");let r=e.shape.length,a=t.shape.length;n==null&&(n=[r-1,a-2]);let s=n;return z(()=>{let i;if(r>a){i=r-a;let l=[];for(let c=0;c<i;++c)l.push(1);t=t.reshape(t.shape.concat(l))}else if(a>r){i=a-r;let l=[];for(let c=0;c<i;++c)l.push(1);e=e.reshape(e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let l=s[0]!==e.shape.length-1,c=s[1]===t.shape.length-1;o=e.matMul(t,l,c)}if(i>0){let l;r>a?l=r+a-3:l=r-1;let c=[];for(let u=l;u<l+i;++u)c.push(u);o=o.squeeze(c)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var o2=class extends Wi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){_.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new De("Dot layer does not support tensors of 4D or higher rank yet.");let r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new B(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(e.length!==2)throw new B(`A \`Dot\` layer must be called on exactly 2 inputs, but 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l2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return zc(()=>Pp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};l2.className="GaussianNoise";re.registerClass(l2);var u2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return z(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.rate>0&&this.rate<1?zc(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return n.mul(Pp(n.shape,1,r))},()=>n,t.training||!1):n})}};u2.className="GaussianDropout";re.registerClass(u2);var c2=class extends qe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return z(()=>{let n=t.training==null?!1:t.training,r=Pe(e),a=r.shape,s=a.length,i=Cr(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Mi(1,s);l[o]=a[o];let c=i.slice();c.sort();let u=!_.arraysEqual(c,Cr(0,s).slice(0,s-1)),h=()=>{if(u){let y=this.movingMean.read().reshape(l),A=this.movingVariance.read().reshape(l),g=this.center?this.beta.read().reshape(l):null,x=this.scale?this.gamma.read().reshape(l):null;return Kc(r,y,A,g,x,this.epsilon)}else return 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extends qe{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Ka(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(a=>e[a]),r=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,r):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,r):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),r=n.shape,a=r.length;return z(()=>{let s=!0,{mean:i,variance:o}=Dd(n,this.axis,s),l=Mi(1,a);for(let f of this.axis)l[f]=r[f];let c=f=>f!=null&&f.shape.length!==a&&this.axis!==[a-1]?f.reshape(l):f,u=c(this.gamma.read()),h=c(this.beta.read()),d=[],p=[];for(let f=0;f<a;++f)this.axis.indexOf(f)!==-1?(d.push(r[f]),p.push(1)):(d.push(1),p.push(r[f]));return i=i.tile(d),o=o.tile(d),u=u.tile(p),h=h.tile(p),Kc(n,i,o,h,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:St(this.betaInitializer),gammaInitializer:St(this.gammaInitializer),betaRegularizer:ut(this.betaRegularizer),gammaRegularizer:ut(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};d2.className="LayerNormalization";re.registerClass(d2);function pae(e,t,n){return z(()=>{if(e.rank!==4)throw new B(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new B("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Tr()),n!=="channelsLast"&&n!=="channelsFirst")throw new B(`Unknown data format: ${n}. 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s==="max"?i=ac(e,t,n,o):i=Ju(e,t,n,o),a==="channelsFirst"&&(i=Je(i,[0,3,1,2])),i})}function p4(e,t,n,r,a,s){return z(()=>{Ct(a),Yv(s),sr(r),n==null&&(n=[1,1,1]),r==null&&(r="valid"),a==null&&(a=Tr()),s==null&&(s="max"),e=o4(e,a);let i,o=r==="same"?"same":"valid";return s==="max"?i=Fm(e,t,n,o):i=gm(e,t,n,o),a==="channelsFirst"&&(i=Je(i,[0,4,1,2,3])),i})}var f4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new B(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ht(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new B(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,sr(this.padding),this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){e=st(e);let t=$r(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return z(()=>{this.invokeCallHook(e,t),e=Dc(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ja(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},f2=class extends f4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),h0(e,t,n,r,a,"max")}};f2.className="MaxPooling1D";re.registerClass(f2);var m2=class extends f4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return 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t=$r(t,this.poolSize[0],this.padding,this.strides[0]),n=$r(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},y2=class extends m4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),h0(e,t,n,r,a,"max")}};y2.className="MaxPooling2D";re.registerClass(y2);var A2=class extends m4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),h0(e,t,n,r,a,"avg")}};A2.className="AveragePooling2D";re.registerClass(A2);var y4=class extends qe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new B(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ht(this.poolSize,"poolSize"),Ht(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),sr(this.padding),this.inputSpec=[new Ft({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],r=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=$r(t,this.poolSize[0],this.padding,this.strides[0]),n=$r(n,this.poolSize[1],this.padding,this.strides[1]),r=$r(r,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return z(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},g2=class extends y4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),p4(e,t,n,r,a,"max")}};g2.className="MaxPooling3D";re.registerClass(g2);var x2=class extends y4{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Ct(a),sr(r),p4(e,t,n,r,a,"avg")}};x2.className="AveragePooling3D";re.registerClass(x2);var A4=class extends qe{constructor(e){super(e);this.inputSpec=[new Ft({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new De}},w2=class extends A4{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return It(n,1)})}};w2.className="GlobalAveragePooling1D";re.registerClass(w2);var b2=class extends A4{constructor(e){super(e||{})}call(e,t){return z(()=>{let n=Pe(e);return Cn(n,1)})}};b2.className="GlobalMaxPooling1D";re.registerClass(b2);var g4=class extends qe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Ft({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new De}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},_2=class extends g4{call(e,t){return z(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};_2.className="GlobalAveragePooling2D";re.registerClass(_2);var v2=class extends g4{call(e,t){return z(()=>{let n=Pe(e);return 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e(s)}},k2=class extends x4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new B(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return z(()=>(e=Pe(e),h4((n,r)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};k2.className="TimeDistributed";re.registerClass(k2);function fae(e){$i(Ite,"BidirectionalMergeMode",e)}var mae="concat",I2=class extends x4{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Fr(n),t.goBackwards=t.goBackwards!==!0;let r={};if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Fr(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?mae:e.mergeMode,fae(this.mergeMode),e.weights)throw new De("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,r,a;return this.returnState&&(a=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,r=[n]):this.mergeMode==null?r=[n,n.slice()]:r=[n],this.returnState?this.mergeMode==null?r.concat(a).concat(a.slice()):[n].concat(a).concat(a.slice()):Fn(r)}apply(e,t){let n=t==null?null:t.initialState,r=t==null?null:t.constants;t==null&&(t={});let a=c4(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&r==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let l=n.length;if(l%2>0)throw new B("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let c=n.map(u=>new Ft({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),i.push(...c)}if(r!=null)throw new De("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Mr;for(let l of s)if(l instanceof Mr!==o)throw new B("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),c=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=c;let h=super.apply(l,t);return this.inputSpec=u,h}else return super.apply(e,t)}call(e,t){return z(()=>{let n=t.initialState,r,a;if(n==null)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),l=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(r)&&(s=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Wn(a,1));let 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r=k("elementShape",e,t,n),a=k("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,n),o=Nae(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=k("tensorListId",e,t,n),a=k("indices",e,t,n),s=k("elementShape",e,t,n),i=k("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=k("tensorListId",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=k("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=k("tensor",e,t,n),a=k("elementShape",e,t,n),s=k("elementDType",e,t,n),i=Iae(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=k("tensorListId",e,t,n),a=n.getTensorList(r.id),s=k("dtype",e,t,n),i=k("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=k("tensorListId",e,t,n),a=k("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let 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qae(e,t,n){let{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map(u=>jn(u)[0]).map(u=>e.nodes[u]),o=e.initNodes;i.forEach(u=>{r.has(u.name)&&s.push(u)}),e.weights.forEach(u=>{r.has(u.name)&&s.push(u)}),o!=null&&o.forEach(u=>{r.has(u.name)&&s.push(u)});let l=new Set,c=[];for(;s.length>0;){let u=s.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(h=>{!l.has(h.name)&&r.has(h.name)&&h.inputs.every(d=>l.has(d.name))&&s.push(h)})}return c}var Xae=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Kae=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Zae=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function D4(e){return Xae.indexOf(e.op)>=0}function Hae(e){return Kae.indexOf(e.op)>=0}function Gae(e){return Zae.indexOf(e.op)>=0}var N2=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=ga(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!Fn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!Fn(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=jn(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);v.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&v.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=jn(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=jn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Yae=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Jae="?tfjs-format=file",Qae="model.json",O4=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Yae}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=kn.browserHTTPRequest(e,this.loadOptions);else{let t=kn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(kn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=kn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new N2(S4.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=S4.Instance.transformGraph(e.modelInitializer);this.initializer=new N2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=kn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof We)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Qae}${Jae}`);let n=new O4(e,t);return await n.load(),n}var ese="3.3.0",z4={};Me(z4,{CSVDataset:()=>L4,Dataset:()=>ql,FileDataSource:()=>W4,TextLineDataset:()=>P4,URLDataSource:()=>B4,array:()=>tse,csv:()=>rse,func:()=>ase,generator:()=>sse,microphone:()=>ose,version_data:()=>lse,webcam:()=>ise,zip:()=>nse});var use=Qi(Jg()),cse=Qi(Jg());function hse(e,t){return d0(e,t)}function d0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Xl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=d0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function dse(e,t=j4){return V4(e,t)}function V4(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Xl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=V4(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function j4(e){return e===null?null:Xl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function U4(e,t){let n=new Map;d0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let s=await a;n.set(r,s)}}return d0(e,t,n)}function Xl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof We))}function fse(e){return e==null||pse(e)||Array.isArray(e)||typeof e=="object"&&e instanceof We||v.isTypedArray(e)}function pse(e){return e===null||typeof e!="object"&&typeof e!="function"}function Ase(e){return hse(e,mse)}function mse(e){return e instanceof We?{value:e.clone(),recurse:!1}:Xl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var H4=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},S2=class extends H4{constructor(){super(S2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};S2.INITIAL_CAPACITY=32;function G4(e){return new yse(e)}function T2(e){return new gse(e)}function xse(e,t){return new q4(e,t)}function bse(e,t=es.FAIL){return new wse(e,t)}var Xt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new Tse(this,e)}filter(e){return new Nse(this,e)}map(e){return new Sse(this,e)}mapAsync(e){return new X4(this,e)}serialMapAsync(e){return new X4(this,e).serial()}flatmap(e){return new Ese(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new Ise(this,e,t)}columnMajorBatch(e,t=!0,n=j4){return this.rowMajorBatch(e,t).map(r=>dse(r,n))}concatenate(e,t){return new q4(G4([this,e]),t)}take(e){return e<0||e==null?this:new kse(this,e)}skip(e){return e<0||e==null?this:new vse(this,e)}prefetch(e){return new K4(this,e)}shuffle(e,t){return new Cse(this,e,t)}serial(){return new _se(this)}},yse=class extends Xt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Ase(e),done:!1}}},gse=class extends Xt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: 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this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Ise=class extends Xt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Nse=class extends Xt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},Sse=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},Tse=class extends Xt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},X4=class extends Xt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},E2=class extends Xt{constructor(){super();this.outputQueue=new S2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Ese=class extends E2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return!0}},q4=class extends Xt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},es;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(es||(es={}));var wse=class extends Xt{constructor(e,t=es.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof 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-      ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Un(async()=>(await n.iterator()).columnMajorBatch(e,t,Rse),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Un(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Un(async()=>(await t.iterator()).filter(r=>z(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Un(async()=>(await t.iterator()).map(n=>z(()=>e(n))),this.size)}mapAsync(e){let t=this;return Un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Un(async()=>{let r=T2(async()=>({value:await t.iterator(),done:!1}));return xse(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Un(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=use.alea(t||v.now().toString());return Un(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Un(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};ql.MAX_BUFFER_SIZE=1e4;function Un(e,t=null){return new class extends ql{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function tse(e){return Un(async()=>G4(e),e.length)}function nse(e){if(!Xl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Un(async()=>{let n=await U4(e,r=>{if(r instanceof ql)return{value:r.iterator(),recurse:!1};if(Xl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return bse(n,es.SHORTEST)},t)}function Rse(e){if(e===null)return null;let t=e[0];return fse(t)?{value:Mse(e),recurse:!1}:{value:null,recurse:!0}}function Mse(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof We?cn(e):Ir(e)}var P4=class extends ql{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},p0='"',Gc=Symbol("out"),Z4=Symbol("field"),f0=Symbol("quote"),C2=Symbol("quoteafterquote"),Y4=Symbol("quoteinquote"),L4=class extends ql{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new P4(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Gc;for(let i=0;i<a;i++)switch(s){case Gc:switch(e.charAt(i)){case p0:r=i+1,s=f0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Gc;break;default:s=Z4,r=i;break}break;case Z4:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Gc,r=i+1;break;default:}break;case f0:switch(e.charAt(i)){case p0:s=C2;break;default:}break;case C2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Gc,r=i+1;break;case p0:s=f0;break;default:s=Y4;break}break;case Y4:switch(e.charAt(i)){case p0:s=f0;break;default:}break;default:}if(s===C2?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},J4=class extends Xt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. 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Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=ui.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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t8{constructor(e,t){super();this.upstream=e,this.impl=new Dse(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Dse=class extends E2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Ose=class extends Xt{decodeUTF8(){return new $se(this)}},$se=class extends t8{constructor(e){super();this.upstream=e,this.impl=new zse(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},zse=class extends E2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=_9();this.decoder=new 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Kse=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Zse=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Yse=[33,133,362,263,1,78,308],loe=Kse.map(e=>Xc[e]),uoe=Zse.map(e=>Xc[e]),coe=Yse.map(e=>Xc[e]);var Y2=Qr.leftEyeLower0,J2=Qr.rightEyeLower0,Yl={leftBounds:[Y2[0],Y2[Y2.length-1]],rightBounds:[J2[0],J2[J2.length-1]]},C0={count:468,mouth:13,symmetryLine:[13,Qr.midwayBetweenEyes[0]]},m8={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Jl={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function R0(e,t,n,r){for(let a=0;a<Z2.length;a++){let{key:s,indices:i}=Z2[a],o=Qr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var Q2=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=qc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?E0(r,[0,0]):T0,l=r!==0?i.map(h=>[...f8(h,o),h[2]]):i,c=r!==0?p8(a):T0,u=[...Kl({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+ts(u,c[0]),h[1]+ts(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[Yl.leftBounds[0]][2],r=t[Yl.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=S0(N0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=qc(i),l=ze.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&_r.flags.IS_BROWSER&&(l=ze.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<Jl.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(Jl.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Qr[`${r}EyeUpper0`][Jl.upperCenter]][2],s=t[Qr[`${r}EyeLower0`][Jl.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=c8({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=N0(o),c=S0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&_r.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=C0.count?C0.symmetryLine:m8.symmetryLine;u=K2(i.landmarks[_],i.landmarks[x]);let N=Kl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=ze.rotateWithOffset(t,u,0,T);h=E0(-u,N),n.face.mesh.enabled?c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=T0;let _=t.clone();n.face.mesh.enabled?c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.meshSize,this.meshSize]).div(255):c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},_,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let A=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:_,boxSize:x,crop:N}=this.getEyeBox(A,c,Yl.leftBounds[0],Yl.leftBounds[1],!0),{box:T,boxSize:E,crop:F}=this.getEyeBox(A,c,Yl.rightBounds[0],Yl.rightBounds[1]),L=this.irisModel.predict(rt([N,F])).dataSync(),V=L.slice(0,Jl.numCoordinates*3),{rawCoords:j,iris:U}=this.getEyeCoords(V,_,x,!0),X=L.slice(Jl.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,T,E),Y=this.getLeftToRightEyeDepthDifference(A);Math.abs(Y)<30?(R0(A,j,"left",null),R0(A,G,"right",null)):Y<1?R0(A,j,"left",["EyeUpper0","EyeLower0"]):R0(A,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(A,U,"left"),te=this.getAdjustedIrisCoords(A,ee,"right");A=A.concat(ae).concat(te)}let y=this.transformRawCoords(A,i,u,h);i=N0(this.calculateLandmarksBoundingBox(y),1.5);let g=Tn(y);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&_r.flags.IS_BROWSER){let[_,x]=i.landmarks.length>=C0.count?C0.symmetryLine:m8.symmetryLine;u=K2(i.landmarks[_],i.landmarks[x]);let N=Kl({startPoint:i.startPoint,endPoint:i.endPoint}),T=[N[0]/t.shape[2],N[1]/t.shape[1]],E=ze.rotateWithOffset(t,u,0,T);h=E0(-u,N),c=Zl({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let w={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:A},b=S0(i);return this.storedBoxes[o]={...b,landmarks:y,confidence:i.confidence,faceConfidence:f},w}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var tg=class{constructor(t,n,r,a){this.facePipeline=new Q2(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Qr))l[h]=Qr[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxConfidence:Math.round(100*s.boxConfidence)/100,faceConfidence:Math.round(100*s.faceConfidence)/100,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},Kt=[null,null,null];async function ng(e){return!Kt[0]&&e.face.enabled||!Kt[1]&&e.face.mesh.enabled||!Kt[2]&&e.face.iris.enabled?(Kt=await Promise.all([!Kt[0]&&e.face.enabled?u8(e):null,!Kt[1]&&e.face.mesh.enabled?ct(pt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Kt[2]&&e.face.iris.enabled?ct(pt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Kt[1]||!Kt[1].modelUrl?le("load model failed:",e.face.mesh.modelPath):e.debug&&le("load model:",Kt[1].modelUrl)),e.face.iris.enabled&&(!Kt[2]||!Kt[1].modelUrl?le("load model failed:",e.face.iris.modelPath):e.debug&&le("load model:",Kt[2].modelUrl))):e.debug&&(le("cached model:",Kt[0].model.modelUrl),le("cached model:",Kt[1].modelUrl),le("cached model:",Kt[2].modelUrl)),new tg(Kt[0],Kt[1],Kt[2],e)}var A8=zi,y8=Xc;var dg={};Yn(dg,{PoseNet:()=>pg,load:()=>fg});function Jse(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var rg=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=Jse(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function ag(e){return Math.floor(e/2)}var sg=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(ag(t),t);)this.exchange(t,ag(t)),t=ag(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let r=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=r}};function Qse(e,t,n,r,a,s){let[i,o]=s.shape,l=!0,c=Math.max(n-a,0),u=Math.min(n+a+1,i);for(let h=c;h<u;++h){let d=Math.max(r-a,0),p=Math.min(r+a+1,o);for(let f=d;f<p;++f)if(s.get(h,f,e)>t){l=!1;break}if(!l)break}return l}function g8(e,t,n){let[r,a,s]=n.shape,i=new sg(r*a*s,({score:o})=>o);for(let o=0;o<r;++o)for(let l=0;l<a;++l)for(let c=0;c<s;++c){let u=n.get(o,l,c);u<e||Qse(c,u,o,l,t,n)&&i.enqueue({score:u,part:{heatmapY:o,heatmapX:l,id:c}})}return i}var Ql=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],eu=Ql.length,Kc=Ql.reduce((e,t,n)=>(e[t]=n,e),{}),eie=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Soe=eie.map(([e,t])=>[Kc[e],Kc[t]]),x8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function ig(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+eu)}}function M0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=ig(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function og(e,t,n){return e<t?t:e>n?n:e}function w8(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function lg(e,t){return{x:e.x+t.x,y:e.y+t.y}}function b8(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function tie(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+eu)}}function nie(e,t){let n=[];for(let r=0;r<eu;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=tie(a,s,r,t);n.push(o),n.push(i)}return Tn(n,[eu,2])}function _8(e,t,n){return z(()=>e.toTensor().mul(ge(t,"int32")).toFloat().add(nie(e,n)))}function rie(e,t){return z(()=>{let n=e.div(ge(t,"int32"));return e.sub(n.mul(ge(t,"int32")))})}function v8(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(ge(n,"int32")).expandDims(1),o=rie(s,n).expandDims(1);return rt([i,o],1)})}var k8=x8.map(([e,t])=>[Kc[e],Kc[t]]),ug=k8.map(([,e])=>e),I8=k8.map(([e])=>e),aie=16;function sie(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function cg(e,t,n,r){return{y:og(Math.round(e.y/t),0,n-1),x:og(Math.round(e.x/t),0,r-1)}}function N8(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=cg(t.position,s,l,c),h=sie(e,u,i),p=lg(t.position,h);for(let A=0;A<o;A++){let y=cg(p,s,l,c),g=ig(y.y,y.x,n,a);p=lg({x:y.x*s,y:y.y*s},{x:g.x,y:g.y})}let f=cg(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:Ql[n],score:m}}function S8(e,t,n,r,a,s){let i=t.shape[2],o=ug.length,l=new Array(i),{part:c,score:u}=e,h=M0(c,r,n);l[c.id]={score:u,part:Ql[c.id],position:h};for(let d=o-1;d>=0;--d){let p=ug[d],f=I8[d];l[p]&&!l[f]&&(l[f]=N8(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=I8[d],f=ug[d];l[p]&&!l[f]&&(l[f]=N8(d,l[p],f,t,n,r,a))}return l}async function T8(e,t,n){let r=0,a=v8(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=_8(l,aie,o),u=await c.buffer(),d=Array.from(b8(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:Ql[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var iie=1,E8=16;function C8(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return w8(r,n,i.y,i.x)<=t})}function oie(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(C8(e,t,s,o)||(a+=i),a),0)/n.length}function R8(e,t,n,r,a,s,i){let o=[],l=g8(i,iie,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=M0(u.part,E8,t);if(C8(o,c,h,u.part.id))continue;let d=S8(u,e,t,E8,n,r),p=oie(o,c,d);p>i&&o.push({keypoints:d,score:Math.round(100*p)/100})}return o}async function M8(e){return Promise.all(e.map(t=>t.buffer()))}function lie(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:r,part:a,position:s})=>({score:r,part:a,position:{x:Math.trunc(s.x*n),y:Math.trunc(s.y*t)}}))}}function F8(e,[t,n]){let r=e.squeeze(0),a=r.resizeBilinear([t,n]);return r.dispose(),a}function hg(e,[t,n],[r,a]){return e.map(i=>lie(i,t/r,n/a))}var Pi;async function uie(e,t,n,r){return new Promise(async a=>{let s=await M8([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),i=s[0],o=s[1],l=s[2],c=s[3],u=await R8(i,o,l,c,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=hg(u,[e.shape[1],e.shape[2]],[r,r]);a(h)})}async function cie(e,t,n,r){return new Promise(async a=>{let s=await T8(t.heatmapScores,t.offsets,n.body.scoreThreshold),i=hg([s],[e.shape[1],e.shape[2]],[r,r]);a(i)})}var pg=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let r=F8(t,[this.inputSize,this.inputSize]),a=this.baseModel.predict(r,n),s=n.body.maxDetections<2?await cie(t,a,n,this.inputSize):await uie(t,a,n,this.inputSize);return a.heatmapScores.dispose(),a.offsets.dispose(),a.displacementFwd.dispose(),a.displacementBwd.dispose(),r.dispose(),s}dispose(){this.baseModel.dispose()}};async function fg(e){Pi?e.debug&&le("cached model:",Pi.modelUrl):(Pi=await ct(pt(e.modelBasePath,e.body.modelPath)),!Pi||!Pi.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",Pi.modelUrl));let t=new rg(Pi);return new pg(t)}var xg={};Yn(xg,{HandPose:()=>bg,load:()=>_g});function F0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Zc(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function D8(e,t,n){let r=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/r,e.startPoint[0]/a,e.endPoint[1]/r,e.endPoint[0]/a]];return 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n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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j8=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],U8=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var An;async function kg(e){return An?e.debug&&le("cached model:",An.modelUrl):(An=await ct(pt(e.modelBasePath,e.body.modelPath)),An.width=parseInt(An.signature.inputs["input_1:0"].tensorShape.dim[2].size),An.height=parseInt(An.signature.inputs["input_1:0"].tensorShape.dim[1].size),!An||!An.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",An.modelUrl)),An}async function Ig(e,t){if(!An||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=ze.resizeBilinear(e,[An.width,An.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let u=await sn(()=>An.predict(a));s=u.result.find(h=>h.size===195||h.size===155).dataSync(),u.result.forEach(h=>h.dispose()),mn("blazepose",u)}else{let u=await An.predict(a);s=u.find(h=>h.size===195||h.size===155).dataSync(),u.forEach(h=>h.dispose())}a.dispose();let i=[],o=s.length===195?j8:U8,l=5;for(let 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r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=lse(r,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let r=I("tensorListId",e,t,n),a=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{let r=I("tensorListId",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{let r=I("tensor",e,t,n),a=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=ose(r,a,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let r=I("tensorListId",e,t,n),a=n.getTensorList(r.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[a.concat(s,i)]}case"TensorListPushBack":{let r=I("tensorListId",e,t,n),a=I("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function e8(e,t,n,r){let a=new Set,s=[],i=null,o=null,l=new Set,c=Object.keys(e).map(d=>Un(d)[0]),u=[];r!=null&&(u=r.map(d=>Un(d.name)[0]));let h=[...t];for(;h.length>0;){let d=h.pop();if((Q4(d)||Nse(d)||Tse(d))&&i==null&&(i=d,o=i.children.map(p=>p.name).filter(p=>a.has(p))),a.add(d.name),n[d.name]==null&&c.indexOf(d.name)===-1&&u.indexOf(d.name)===-1){if(d.inputs.length===0){s.push(d.name);continue}d.inputs.forEach(p=>{l.has(p.name)||(l.add(p.name),h.push(p))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function 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Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new W2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(r=>r.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(a=>a.name).sort(),r=t.map(a=>a.name).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){let n=e8(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${r}]`)}return Ese(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let r=n.map(u=>this.graph.nodes[Un(u)[0]]),a=t.map(u=>Un(u)[0]),s=a.map(u=>this.graph.nodes[u]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(r,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},c={};return z(()=>{let u=new J4(this.weightMap,l,c,this.functionExecutorMap),h=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,y]=Un(f),A=[];A[y]=e[f],h[m]=A});let d=this.getFrozenTensorIds(h),p={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let y=Y4(m,h,u,this._resourceManager);if(_.isPromise(y))throw new Error(`The execution of the op '${m.op}' returned a promise. 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You can use model.execute() instead.");let A=o.filter(g=>!Q4(g)&&!An(g.name,p,t)).map(g=>g.name);if(A.length>0){let g="";throw u!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${A}] from the provided inputs [${a}]. Consider providing the following inputs: [${c}]. ${g}`)}return p}processStack(e,t,n,r,a,s,i,o,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let h="";if(u.node.op==="Enter"&&I("isConstant",u.node,r,n)&&([h]=xa(u.node.name,n)),r[u.node.name]==null){let d=Y4(u.node,r,n,this._resourceManager);h||([h]=xa(u.node.name,n));let p=n.currentContext;_.isPromise(d)?c.push(d.then(f=>(r[h]=f,n.currentContext=p,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l),f))):(r[h]=d,this.checkTensorForDisposal(h,u.node,r,n,s,i,o),this.processChildNodes(u.node,t,n,r,a,l))}else this.processChildNodes(u.node,t,n,r,a,l)}return c}processChildNodes(e,t,n,r,a,s){e.children.forEach(i=>{let[o]=xa(i.name,n);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!An(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(l=>!!An(l,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[r]=Un(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,l)=>s[l]===-1||s[l]===o);_.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&_.assert(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let r=this._signature.inputs[n];t[r.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[r]=Un(n);return this.graph.nodes[r]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Un(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Fse=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},$se="?tfjs-format=file",Dse="model.json",t8=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Fse}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Nn.browserHTTPRequest(e,this.loadOptions);else{let t=Nn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Nn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let r=Nn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new W2(G4.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=G4.Instance.transformGraph(e.modelInitializer);this.initializer=new W2(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Nn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof ze)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,r)=>(t[n]=e[r],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function ct(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Dse}${$se}`);let n=new t8(e,t);return await n.load(),n}var Ose="3.4.0",n8={};Me(n8,{CSVDataset:()=>a8,Dataset:()=>Yl,FileDataSource:()=>s8,TextLineDataset:()=>r8,URLDataSource:()=>i8,array:()=>zse,csv:()=>Lse,func:()=>Wse,generator:()=>Bse,microphone:()=>jse,version_data:()=>Use,webcam:()=>Vse,zip:()=>Pse});var Hse=no(d5()),Gse=no(d5());function qse(e,t){return m0(e,t)}function m0(e,t,n=new Map,r=new Set){if(e==null)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(Jl(e)){let s=Array.isArray(e)?[]:{};r.add(e);for(let i in e){let o=e[i],l=m0(o,t,n,r);s[i]=l}return r.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,a.value),a.value}function Xse(e,t=l8){return o8(e,t)}function o8(e,t,n=new Set){let r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(Jl(r)){let s=Array.isArray(r)?[]:{};n.add(r);for(let i in r){let o=e.map(c=>c[i]),l=o8(o,t,n);s[i]=l}return n.delete(r),s}else throw new Error(`Can't recurse into non-iterable type: ${r}`);else return a.value}function l8(e){return e===null?null:Jl(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function u8(e,t){let n=new Map;m0(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(_.isPromise(a)){let s=await a;n.set(r,s)}}return m0(e,t,n)}function Jl(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof ze))}function Zse(e){return e==null||Kse(e)||Array.isArray(e)||typeof e=="object"&&e instanceof ze||_.isTypedArray(e)}function Kse(e){return e===null||typeof e!="object"&&typeof e!="function"}function Jse(e){return qse(e,Yse)}function Yse(e){return e instanceof ze?{value:e.clone(),recurse:!1}:Jl(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var c8=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},B2=class extends c8{constructor(){super(B2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let r=0;r<n;r++)t[r]=this.get(this.wrap(this.begin+r));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};B2.INITIAL_CAPACITY=32;function h8(e){return new Qse(e)}function V2(e){return new eie(e)}function tie(e,t){return new d8(e,t)}function rie(e,t=ns.FAIL){return new nie(e,t)}var Gt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new cie(this,e)}filter(e){return new lie(this,e)}map(e){return new uie(this,e)}mapAsync(e){return new p8(this,e)}serialMapAsync(e){return new p8(this,e).serial()}flatmap(e){return new hie(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new oie(this,e,t)}columnMajorBatch(e,t=!0,n=l8){return this.rowMajorBatch(e,t).map(r=>Xse(r,n))}concatenate(e,t){return new d8(h8([this,e]),t)}take(e){return e<0||e==null?this:new iie(this,e)}skip(e){return e<0||e==null?this:new sie(this,e)}prefetch(e){return new f8(this,e)}shuffle(e,t){return new die(this,e,t)}serial(){return new aie(this)}},Qse=class extends Gt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Jse(e),done:!1}}},eie=class extends Gt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},aie=class extends Gt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},sie=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;_e(e.value)}return this.upstream.next()}},iie=class extends Gt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},oie=class extends Gt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},lie=class extends Gt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;_e(e.value)}}},uie=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},cie=class extends Gt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},p8=class extends Gt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=vr.getTensorsInContainer(e.value),n=await this.transform(e.value),r=vr.getTensorsInContainer(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return{value:n,done:!1}}},j2=class extends Gt{constructor(){super();this.outputQueue=new B2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},hie=class extends j2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=vr.getTensorsInContainer(e.value),n=this.transform(e.value),r=vr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let a of t)vr.isTensorInList(a,r)||a.dispose();return!0}},d8=class extends Gt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},ns;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ns||(ns={}));var nie=class extends Gt{constructor(e,t=ns.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function r(s){return s instanceof Gt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await u8(this.iterators,r);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case ns.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ns.SHORTEST:return{value:null,done:!0};case ns.LONGEST:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},f8=class extends Gt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new c8(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},die=class extends f8{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Gse.alea(n||_.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Yl=class{constructor(){this.size=null}batch(e,t=!0){let n=this;_.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let r;return this.size===Infinity||this.size==null?r=this.size:t?r=Math.ceil(this.size/e):r=Math.floor(this.size/e),Hn(async()=>(await n.iterator()).columnMajorBatch(e,t,pie),r)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Hn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Hn(async()=>(await t.iterator()).filter(r=>z(()=>e(r))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Hn(async()=>(await t.iterator()).map(n=>z(()=>e(n))),this.size)}mapAsync(e){let t=this;return Hn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Hn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Hn(async()=>{let r=V2(async()=>({value:await t.iterator(),done:!1}));return tie(r.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Hn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let r=this,a=Hse.alea(t||_.now().toString());return Hn(async()=>{let s=a.int32();return n&&(s+=a.int32()),(await r.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Hn(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Yl.MAX_BUFFER_SIZE=1e4;function Hn(e,t=null){return new class extends Yl{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function zse(e){return Hn(async()=>h8(e),e.length)}function Pse(e){if(!Jl(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Hn(async()=>{let n=await u8(e,r=>{if(r instanceof Yl)return{value:r.iterator(),recurse:!1};if(Jl(r))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return rie(n,ns.SHORTEST)},t)}function pie(e){if(e===null)return null;let t=e[0];return Zse(t)?{value:fie(e),recurse:!1}:{value:null,recurse:!0}}function fie(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof ze?cn(e):Ir(e)}var r8=class extends Yl{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},y0='"',Jc=Symbol("out"),m8=Symbol("field"),A0=Symbol("quote"),U2=Symbol("quoteafterquote"),y8=Symbol("quoteinquote"),a8=class extends Yl{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new r8(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(_.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&_.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((r,a)=>(r[a]=r[a]+1||1,r),{}),n=Object.keys(t).filter(r=>t[r]>1);if(_.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let r of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(r)===-1)throw new Error('The key "'+r+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},r={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let c=Number(o);if(isNaN(c))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=c;else switch(i.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(o);break;default:l=c}}i&&i.isLabel?r[s]=l:n[s]=l}}return Object.keys(r).length===0?n:{xs:n,ys:r}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],r=0,a=e.length,s=Jc;for(let i=0;i<a;i++)switch(s){case Jc:switch(e.charAt(i)){case y0:r=i+1,s=A0;break;case this.delimiter:if(r=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=Jc;break;default:s=m8,r=i;break}break;case m8:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i)),s=Jc,r=i+1;break;default:}break;case A0:switch(e.charAt(i)){case y0:s=U2;break;default:}break;case U2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(r,i-1)),s=Jc,r=i+1;break;case y0:s=A0;break;default:s=y8;break}break;case y8:switch(e.charAt(i)){case y0:s=A0;break;default:}break;default:}if(s===U2?n.push(e.substring(r,a-1)):n.push(e.substring(r)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A8=class extends Gt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new A8(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let r=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(r,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let r=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(r,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(r=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((r,a)=>n.set(r,a*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(_.sizeFromShape(t));return n.set(e,n.length-e.length),Ir(n,t)}},g8=class extends Gt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=sn([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,r=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-n)/2,s=(1-r)/2,i=a+n,o=r+s;this.cropBox=Rn([s,a,o,i],[1,4])}else this.cropBox=Rn([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new g8(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&_.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=pi.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return z(()=>{let t=Jt(ge(e,"float32"),0),n;n=Le.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let r=n.shape;return H(n,r.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},x8=class{},w8=class extends Gt{split(e){return new mie(this,e)}},mie=class extends w8{constructor(e,t){super();this.upstream=e,this.impl=new yie(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},yie=class extends j2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},gie=class extends Gt{decodeUTF8(){return new Aie(this)}},Aie=class extends w8{constructor(e){super();this.upstream=e,this.impl=new xie(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},xie=class extends j2{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=D9();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return J().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},b8=class extends gie{constructor(e,t={}){super();this.file=e,this.options=t,_.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let r=new FileReader;r.onload=s=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},r.onabort=s=>t(new Error("Aborted")),r.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function bie(e,t={}){let n,r;typeof e=="string"?n=e:(n=e.url,r=wie(e));let a=await _.fetch(n,r);if(a.ok){let s=new Uint8Array(await a.arrayBuffer());return new b8(s,t)}else throw new Error(a.statusText)}var wie=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function _8(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var s8=class extends x8{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(_8(this.input)&&J().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new b8(this.input,this.options)}},i8=class extends x8{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return _8(this.url)?new s8(this.url,this.fileOptions).iterator():bie(this.url,this.fileOptions)}};function Lse(e,t={}){return new a8(new i8(e),t)}function Wse(e){let t=V2(e);return Hn(async()=>t)}function Bse(e){return Hn(async()=>{let t=await e();return V2(()=>t.next())})}async function Vse(e,t){return g8.create(e,t)}async function jse(e){return A8.create(e)}var Use="3.4.0",_ie={tfjs:(bf==null?void 0:bf.version)||void 0,"tfjs-core":(_f==null?void 0:_f.version)||void 0,"tfjs-data":(vf==null?void 0:vf.version)||void 0,"tfjs-layers":(kf==null?void 0:kf.version)||void 0,"tfjs-converter":(If==null?void 0:If.version)||void 0,"tfjs-backend-cpu":w_||void 0,"tfjs-backend-webgl":V3||void 0,"tfjs-backend-wasm":$v||void 0};var 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Rie=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Mie=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Fie=[33,133,362,263,1,78,308],Uoe=Rie.map(e=>eh[e]),Hoe=Mie.map(e=>eh[e]),Goe=Fie.map(e=>eh[e]);var hg=Qr.leftEyeLower0,dg=Qr.rightEyeLower0,tu={leftBounds:[hg[0],hg[hg.length-1]],rightBounds:[dg[0],dg[dg.length-1]]},F0={count:468,mouth:13,symmetryLine:[13,Qr.midwayBetweenEyes[0]]},$8={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},nu={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function $0(e,t,n,r){for(let a=0;a<cg.length;a++){let{key:s,indices:i}=cg[a],o=Qr[`${n}${s}`];if(!r||r.includes(s))for(let l=0;l<i.length;l++){let c=i[l];e[o[l]]=[t[c][0],t[c][1],(t[c][2]+e[o[l]][2])/2]}}}var pg=class{constructor(t,n,r){var a,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=r,this.boxSize=((a=t==null?void 0:t.model)==null?void 0:a.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(r==null?void 0:r.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,r,a){let s=Qc({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(h=>[s[0]/this.meshSize*(h[0]-this.meshSize/2),s[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=r!==0?M0(r,[0,0]):R0,l=r!==0?i.map(h=>[...F8(h,o),h[2]]):i,c=r!==0?M8(a):R0,u=[...Ql({startPoint:n.startPoint,endPoint:n.endPoint}),1];return l.map(h=>[h[0]+rs(u,c[0]),h[1]+rs(u,c[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[tu.leftBounds[0]][2],r=t[tu.rightBounds[0]][2];return n-r}getEyeBox(t,n,r,a,s=!1){let i=C0(E0(this.calculateLandmarksBoundingBox([t[r],t[a]]),this.irisEnlarge)),o=Qc(i),l=Le.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&_r.flags.IS_BROWSER&&(l=Le.flipLeftRight(l)),{box:i,boxSize:o,crop:l}}getEyeCoords(t,n,r,a=!1){let s=[];for(let i=0;i<nu.numCoordinates;i++){let o=t[i*3],l=t[i*3+1],c=t[i*3+2];s.push([(a?1-o/this.irisSize:o/this.irisSize)*r[0]+n.startPoint[0],l/this.irisSize*r[1]+n.startPoint[1],c])}return{rawCoords:s,iris:s.slice(nu.index)}}getAdjustedIrisCoords(t,n,r){let a=t[Qr[`${r}EyeUpper0`][nu.upperCenter]][2],s=t[Qr[`${r}EyeLower0`][nu.lowerCenter]][2],i=(a+s)/2;return n.map((o,l)=>{let c=i;return l===2?c=a:l===4&&(c=s),[o[0],o[1],c]})}async predict(t,n){let r=!1,a;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(a=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||a&&a.boxes&&(!n.face.mesh.enabled||a.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let i of a.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks,confidence:i.confidence});this.storedBoxes.length>0&&(r=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),r){if(!a||!a.boxes||a.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=E8({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},a.scaleFactor),l=E0(o),c=C0(l),u=this.storedBoxes[i].landmarks.arraySync(),h=this.storedBoxes[i].confidence;this.storedBoxes[i]={...c,confidence:h,landmarks:u}}}a&&a.boxes&&a.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=z(()=>this.storedBoxes.map((i,o)=>{let l=i.confidence,c,u=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&_r.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=F0.count?F0.symmetryLine:$8.symmetryLine;u=ug(i.landmarks[w],i.landmarks[b]);let k=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],E=Le.rotateWithOffset(t,u,0,N);h=M0(-u,k),n.face.mesh.enabled?c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255):c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.boxSize,this.boxSize]).div(255)}else{h=R0;let w=t.clone();n.face.mesh.enabled?c=eu({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.meshSize,this.meshSize]).div(255):c=eu({startPoint:i.startPoint,endPoint:i.endPoint},w,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:i,faceConfidence:null,boxConfidence:l,confidence:i.confidence,image:c};let[,d,p]=this.meshDetector.predict(c),f=d.dataSync()[0];if(f<n.face.detector.minConfidence)return null;let y=H(p,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:w,boxSize:b,crop:k}=this.getEyeBox(y,c,tu.leftBounds[0],tu.leftBounds[1],!0),{box:N,boxSize:E,crop:F}=this.getEyeBox(y,c,tu.rightBounds[0],tu.rightBounds[1]),L=this.irisModel.predict(rt([k,F])).dataSync(),V=L.slice(0,nu.numCoordinates*3),{rawCoords:j,iris:U}=this.getEyeCoords(V,w,b,!0),X=L.slice(nu.numCoordinates*3),{rawCoords:G,iris:ee}=this.getEyeCoords(X,N,E),Y=this.getLeftToRightEyeDepthDifference(y);Math.abs(Y)<30?($0(y,j,"left",null),$0(y,G,"right",null)):Y<1?$0(y,j,"left",["EyeUpper0","EyeLower0"]):$0(y,G,"right",["EyeUpper0","EyeLower0"]);let ae=this.getAdjustedIrisCoords(y,U,"left"),te=this.getAdjustedIrisCoords(y,ee,"right");y=y.concat(ae).concat(te)}let A=this.transformRawCoords(y,i,u,h);i=E0(this.calculateLandmarksBoundingBox(A),1.5);let g=Rn(A);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&_r.flags.IS_BROWSER){let[w,b]=i.landmarks.length>=F0.count?F0.symmetryLine:$8.symmetryLine;u=ug(i.landmarks[w],i.landmarks[b]);let k=Ql({startPoint:i.startPoint,endPoint:i.endPoint}),N=[k[0]/t.shape[2],k[1]/t.shape[1]],E=Le.rotateWithOffset(t.toFloat(),u,0,N);h=M0(-u,k),c=eu({startPoint:i.startPoint,endPoint:i.endPoint},E,[this.meshSize,this.meshSize]).div(255)}let x={coords:g,box:i,faceConfidence:f,boxConfidence:l,image:c,rawCoords:y},v=C0(i);return this.storedBoxes[o]={...v,landmarks:A,confidence:i.confidence,faceConfidence:f},x}));return s=s.filter(i=>i!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s,landmarks:t}}};var mg=class{constructor(t,n,r,a){this.facePipeline=new pg(t,n,r),this.config=a}async estimateFaces(t,n){let r=await this.facePipeline.predict(t,n),a=[];for(let s of r||[]){if(s.isDisposedInternal)continue;let i=s.coords?s.coords.arraySync():[],o=i.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),l={};if(i&&i.length>0)for(let h of Object.keys(Qr))l[h]=Qr[h].map(d=>i[d]);let c=s.box?[Math.max(0,s.box.startPoint[0]),Math.max(0,s.box.startPoint[1]),Math.min(t.shape[2],s.box.endPoint[0])-Math.max(0,s.box.startPoint[0]),Math.min(t.shape[1],s.box.endPoint[1])-Math.max(0,s.box.startPoint[1])]:0,u=s.box?[s.box.startPoint[0]/t.shape[2],s.box.startPoint[1]/t.shape[1],(s.box.endPoint[0]-s.box.startPoint[0])/t.shape[2],(s.box.endPoint[1]-s.box.startPoint[1])/t.shape[1]]:[];a.push({confidence:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxConfidence:Math.round(100*s.boxConfidence)/100,faceConfidence:Math.round(100*s.faceConfidence)/100,box:c,boxRaw:u,mesh:i,meshRaw:o,annotations:l,image:s.image?s.image.clone():null}),s.coords&&s.coords.dispose(),s.image&&s.image.dispose()}return a}},qt=[null,null,null];async function yg(e){return!qt[0]&&e.face.enabled||!qt[1]&&e.face.mesh.enabled||!qt[2]&&e.face.iris.enabled?(qt=await Promise.all([!qt[0]&&e.face.enabled?T8(e):null,!qt[1]&&e.face.mesh.enabled?ct(pt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!qt[2]&&e.face.iris.enabled?ct(pt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!qt[1]||!qt[1].modelUrl?le("load model failed:",e.face.mesh.modelPath):e.debug&&le("load model:",qt[1].modelUrl)),e.face.iris.enabled&&(!qt[2]||!qt[1].modelUrl?le("load model failed:",e.face.iris.modelPath):e.debug&&le("load model:",qt[2].modelUrl))):e.debug&&(le("cached model:",qt[0].model.modelUrl),le("cached model:",qt[1].modelUrl),le("cached model:",qt[2].modelUrl)),new mg(qt[0],qt[1],qt[2],e)}var D8=Bi,O8=eh;var Sg={};Jn(Sg,{PoseNet:()=>Ng,load:()=>Tg});function $ie(e){let[t,n,r,a]=e;return{offsets:t,heatmap:n,displacementFwd:r,displacementBwd:a}}var Ag=class{constructor(t){this.model=t}predict(t){return z(()=>{let r=t.toFloat().div(127.5).sub(1).expandDims(0),s=this.model.predict(r).map(o=>o.squeeze([0])),i=$ie(s);return{heatmapScores:i.heatmap.sigmoid(),offsets:i.offsets,displacementFwd:i.displacementFwd,displacementBwd:i.displacementBwd}})}dispose(){this.model.dispose()}};function gg(e){return Math.floor(e/2)}var xg=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(gg(t),t);)this.exchange(t,gg(t)),t=gg(t)}sink(t){for(;2*t<=this.numberOfElements;){let 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ru=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],au=ru.length,th=ru.reduce((e,t,n)=>(e[t]=n,e),{}),Oie=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],ule=Oie.map(([e,t])=>[th[e],th[t]]),P8=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function wg(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+au)}}function D0(e,t,n){let{heatmapY:r,heatmapX:a,id:s}=e,{y:i,x:o}=wg(r,a,s,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function bg(e,t,n){return e<t?t:e>n?n:e}function L8(e,t,n,r){let a=n-e,s=r-t;return a*a+s*s}function _g(e,t){return{x:e.x+t.x,y:e.y+t.y}}function W8(e,t){let n=t.shape[0],r=new Float32Array(n);for(let a=0;a<n;a++){let s=t.get(a,0),i=t.get(a,1);r[a]=e.get(s,i,a)}return r}function zie(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+au)}}function Pie(e,t){let n=[];for(let r=0;r<au;r++){let a=e.get(r,0).valueOf(),s=e.get(r,1).valueOf(),{x:i,y:o}=zie(a,s,r,t);n.push(o),n.push(i)}return Rn(n,[au,2])}function B8(e,t,n){return z(()=>e.toTensor().mul(xe(t,"int32")).toFloat().add(Pie(e,n)))}function Lie(e,t){return z(()=>{let n=e.div(xe(t,"int32"));return e.sub(n.mul(xe(t,"int32")))})}function V8(e){let[t,n,r]=e.shape;return z(()=>{let s=e.reshape([t*n,r]).argMax(0),i=s.div(xe(n,"int32")).expandDims(1),o=Lie(s,n).expandDims(1);return rt([i,o],1)})}var j8=P8.map(([e,t])=>[th[e],th[t]]),vg=j8.map(([,e])=>e),U8=j8.map(([e])=>e),Wie=16;function Bie(e,t,n){let r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}function kg(e,t,n,r){return{y:bg(Math.round(e.y/t),0,n-1),x:bg(Math.round(e.x/t),0,r-1)}}function H8(e,t,n,r,a,s,i,o=2){let[l,c]=r.shape,u=kg(t.position,s,l,c),h=Bie(e,u,i),p=_g(t.position,h);for(let y=0;y<o;y++){let A=kg(p,s,l,c),g=wg(A.y,A.x,n,a);p=_g({x:A.x*s,y:A.y*s},{x:g.x,y:g.y})}let f=kg(p,s,l,c),m=r.get(f.y,f.x,n);return{position:p,part:ru[n],score:m}}function G8(e,t,n,r,a,s){let i=t.shape[2],o=vg.length,l=new Array(i),{part:c,score:u}=e,h=D0(c,r,n);l[c.id]={score:u,part:ru[c.id],position:h};for(let d=o-1;d>=0;--d){let p=vg[d],f=U8[d];l[p]&&!l[f]&&(l[f]=H8(d,l[p],f,t,n,r,s))}for(let d=0;d<o;++d){let p=U8[d],f=vg[d];l[p]&&!l[f]&&(l[f]=H8(d,l[p],f,t,n,r,a))}return l}async function q8(e,t,n){let r=0,a=V8(e),s=await Promise.all([e.buffer(),t.buffer(),a.buffer()]),i=s[0],o=s[1],l=s[2],c=B8(l,Wie,o),u=await c.buffer(),d=Array.from(W8(i,l)).map((f,m)=>(r+=f,{position:{y:u.get(m,0),x:u.get(m,1)},part:ru[m],score:f})),p=d.filter(f=>f.score>n);return a.dispose(),c.dispose(),{keypoints:p,score:r/d.length}}var Vie=1,X8=16;function K8(e,t,{x:n,y:r},a){return e.some(({keypoints:s})=>{let i=s[a].position;return L8(r,n,i.y,i.x)<=t})}function jie(e,t,n){return n.reduce((a,{position:s,score:i},o)=>(K8(e,t,s,o)||(a+=i),a),0)/n.length}function Z8(e,t,n,r,a,s,i){let o=[],l=z8(i,Vie,e),c=a^2;for(;o.length<s&&!l.empty();){let u=l.dequeue(),h=D0(u.part,X8,t);if(K8(o,c,h,u.part.id))continue;let d=G8(u,e,t,X8,n,r),p=jie(o,c,d);p>i&&o.push({keypoints:d,score:Math.round(100*p)/100})}return o}async function Y8(e){return Promise.all(e.map(t=>t.buffer()))}function 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n=t.map(i=>i[0]),r=t.map(i=>i[1]),a=[Math.min(...n),Math.min(...r)],s=[Math.max(...n),Math.max(...r)];return{startPoint:a,endPoint:s}}};var 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lk=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],uk=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var xn;async function Pg(e){return xn?e.debug&&le("cached model:",xn.modelUrl):(xn=await ct(pt(e.modelBasePath,e.body.modelPath)),xn.width=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[2].size),xn.height=parseInt(xn.signature.inputs["input_1:0"].tensorShape.dim[1].size),!xn||!xn.modelUrl?le("load model failed:",e.body.modelPath):e.debug&&le("load model:",xn.modelUrl)),xn}async function Lg(e,t){if(!xn||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},r=Le.resizeBilinear(e,[xn.width,xn.height],!1),a=me(r,[255]);r.dispose();let s;if(t.profile){let u=await an(()=>xn.predict(a));s=u.result.find(h=>h.size===195||h.size===155).dataSync(),u.result.forEach(h=>h.dispose()),gn("blazepose",u)}else{let u=await xn.predict(a);s=u.find(h=>h.size===195||h.size===155).dataSync(),u.forEach(h=>h.dispose())}a.dispose();let i=[],o=s.length===195?lk:uk,l=5;for(let 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ht={backend:"webgl",modelBasePath:"../models/",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"},age:{enabled:!1,modelPath:"age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"mobileface.json"}},body:{enabled:!0,modelPath:"posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var Li={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function B0(e,t,n,r=0,a){e.fillStyle=a.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:a.color,e.beginPath(),e.arc(t,n,a.pointSize,0,2*Math.PI),e.fill()}function tu(e,t,n,r,a,s){if(e.beginPath(),s.useCurves){let i=(t+t+r)/2,o=(n+n+a)/2;e.ellipse(i,o,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+r-s.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+s.roundRect),e.lineTo(t+r,n+a-s.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-s.roundRect,n+a),e.lineTo(t+s.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function $g(e,t=[],n){if(!(t===void 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c=o[1]>0?`#${o[1]}`:"",u=`${o[0]} ${c}: ${l[1]}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(u,8,2+s*r.lineHeight)),a.fillStyle=r.labelColor,a.fillText(u,6,0+s*r.lineHeight),s+=1}}}async function Y8(e,t,n){let r=Jn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a)for(let s of t){a.font=r.font,a.strokeStyle=r.color,a.fillStyle=r.color,r.drawBoxes&&(r.useRawBoxes?tu(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):tu(a,s.box[0],s.box[1],s.box[2],s.box[3],r));let i=[];if(i.push(`face confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}s.rotation&&s.rotation.angle&&s.rotation.angle.roll&&i.push(`roll: ${Math.trunc(100*s.rotation.angle.roll)/100} yaw:${Math.trunc(100*s.rotation.angle.yaw)/100} pitch:${Math.trunc(100*s.rotation.angle.pitch)/100}`),i.length===0&&i.push("face"),a.fillStyle=r.color;for(let o=i.length-1;o>=0;o--){let l=Math.max(s.box[0],0),c=o*r.lineHeight+s.box[1];r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i[o],l+5,c+16)),a.fillStyle=r.labelColor,a.fillText(i[o],l+4,c+15)}if(a.lineWidth=1,s.mesh&&s.mesh.length>0){if(r.drawPoints)for(let o of s.mesh)B0(a,o[0],o[1],o[2],r);if(r.drawPolygons){a.lineWidth=1;for(let o=0;o<zi.length/3;o++){let l=[zi[o*3+0],zi[o*3+1],zi[o*3+2]].map(c=>s.mesh[c]);$g(a,l,r)}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;a.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;a.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}}}}}var rs=[];async function J8(e,t,n){let r=Jn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round";for(let s=0;s<t.length;s++){if(!rs[s]&&r.bufferedOutput&&(rs[s]={...t[s]}),a.strokeStyle=r.color,a.lineWidth=r.lineWidth,r.drawPoints)for(let i=0;i<t[s].keypoints.length;i++)a.fillStyle=r.useDepth&&t[s].keypoints[i].position.z?`rgba(${127.5+2*t[s].keypoints[i].position.z}, ${127.5-2*t[s].keypoints[i].position.z}, 255, 0.5)`:r.color,r.bufferedOutput?(rs[s].keypoints[i][0]=(rs[s].keypoints[i][0]+t[s].keypoints[i].position.x)/2,rs[s].keypoints[i][1]=(rs[s].keypoints[i][1]+t[s].keypoints[i].position.y)/2,B0(a,rs[s].keypoints[i][0],rs[s].keypoints[i][1],0,r)):B0(a,t[s].keypoints[i].position.x,t[s].keypoints[i].position.y,0,r);if(r.drawLabels&&(a.font=r.font,t[s].keypoints))for(let i of t[s].keypoints)a.fillStyle=r.useDepth&&i.position.z?`rgba(${127.5+2*i.position.z}, ${127.5-2*i.position.z}, 255, 0.5)`:r.color,a.fillText(`${i.part}`,i.position.x+4,i.position.y+4);if(r.drawPolygons&&t[s].keypoints){let i,o=[];o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),o.length===4&&$g(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftKnee"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftAnkle"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftHeel"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftFoot"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightHip"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightKnee"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightAnkle"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightHeel"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightFoot"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="leftShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftElbow"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftWrist"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="leftPalm"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r),o.length=0,i=t[s].keypoints.find(l=>l.part==="rightShoulder"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightElbow"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightWrist"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),i=t[s].keypoints.find(l=>l.part==="rightPalm"),i&&i.score>ht.body.scoreThreshold&&o.push([i.position.x,i.position.y]),Jc(a,o,r)}}}}async function Q8(e,t,n){let r=Jn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round",a.font=r.font;for(let s of t){if(r.drawBoxes&&(a.strokeStyle=r.color,a.fillStyle=r.color,r.useRawBoxes?tu(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):tu(a,s.box[0],s.box[1],s.box[2],s.box[3],r),r.drawLabels&&(r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText("hand",s.box[0]+3,1+s.box[1]+r.lineHeight,s.box[2])),a.fillStyle=r.labelColor,a.fillText("hand",s.box[0]+2,0+s.box[1]+r.lineHeight,s.box[2])),a.stroke()),r.drawPoints&&s.landmarks&&s.landmarks.length>0)for(let i of s.landmarks)a.fillStyle=r.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:r.color,B0(a,i[0],i[1],0,r);if(r.drawPolygons){let i=o=>{if(!!o)for(let l=0;l<o.length;l++)a.lineWidth=r.lineWidth,a.beginPath(),a.strokeStyle=r.useDepth?`rgba(${127.5+2*o[l][2]}, ${127.5-2*o[l][2]}, 255, 0.5)`:r.color,a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()};i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function ek(e,t,n){let r=Jn(Li,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round",a.font=r.font;for(let s of t)if(r.drawBoxes){if(a.strokeStyle=r.color,a.fillStyle=r.color,r.useRawBoxes?tu(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):tu(a,s.box[0],s.box[1],s.box[2],s.box[3],r),r.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+r.lineHeight,s.box[2])),a.fillStyle=r.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+r.lineHeight,s.box[2])}a.stroke()}}}async function wie(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function bie(e,t,n){let r=Jn(Li,n);!t||!e||e instanceof HTMLCanvasElement&&(Y8(e,t.face,r),J8(e,t.body,r),Q8(e,t.hand,r),Z8(e,t.gesture,r),ek(e,t.object,r))}var V0=`
+`),h.brightness=function(w){let b=(w||0)+1;h.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},h.saturation=function(w){let b=(w||0)*2/3+1,k=(b-1)*-.5;h.colorMatrix([b,k,k,0,0,k,b,k,0,0,k,k,b,0,0,0,0,0,1,0])},h.desaturate=function(){h.saturation(-1)},h.contrast=function(w){let b=(w||0)+1,k=-128*(b-1);h.colorMatrix([b,0,0,0,k,0,b,0,0,k,0,0,b,0,k,0,0,0,1,0])},h.negative=function(){h.contrast(-2)},h.hue=function(w){w=(w||0)/180*Math.PI;let b=Math.cos(w),k=Math.sin(w),N=.213,E=.715,F=.072;h.colorMatrix([N+b*(1-N)+k*-N,E+b*-E+k*-E,F+b*-F+k*(1-F),0,0,N+b*-N+k*.143,E+b*(1-E)+k*.14,F+b*-F+k*-.283,0,0,N+b*-N+k*-(1-N),E+b*-E+k*E,F+b*(1-F)+k*F,0,0,0,0,0,1,0])},h.desaturateLuminance=function(){h.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},h.sepia=function(){h.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},h.brownie=function(){h.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},h.vintagePinhole=function(){h.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},h.kodachrome=function(){h.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},h.technicolor=function(){h.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},h.polaroid=function(){h.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},h.shiftToBGR=function(){h.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},h.convolution=function(w){let b=new Float32Array(w),k=1/o,N=1/l,E=v(h.convolution.SHADER);m.uniform1fv(E.uniform.m,b),m.uniform2f(E.uniform.px,k,N),x()},h.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(`
+`),h.detectEdges=function(){h.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},h.sobelX=function(){h.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},h.sobelY=function(){h.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},h.sharpen=function(w){let b=w||1;h.convolution.call(this,[0,-1*b,0,-1*b,1+4*b,-1*b,0,-1*b,0])},h.emboss=function(w){let b=w||1;h.convolution.call(this,[-2*b,-1*b,0,-1*b,1,1*b,0,1*b,2*b])},h.blur=function(w){let b=w/7/o,k=w/7/l,N=v(h.blur.SHADER);m.uniform2f(N.uniform.px,0,k),x(f.INTERMEDIATE),m.uniform2f(N.uniform.px,b,0),x()},h.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;","}"].join(`
+`),h.pixelate=function(w){let b=w/o,k=w/l,N=v(h.pixelate.SHADER);m.uniform2f(N.uniform.size,b,k),x()},h.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(`
+`)}var j0=2048,Re,wt,$t;function qg(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof ze)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof ze)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Wr(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let a=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0,i=a,o=s;if(i>j0&&(i=j0,o=i*s/a),o>j0&&(o=j0,i=o*a/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=a*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/a)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Re||(Re==null?void 0:Re.width)!==i||(Re==null?void 0:Re.height)!==o)&&(Re=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Re==null?void 0:Re.width)!==i&&(Re.width=i),(Re==null?void 0:Re.height)!==o&&(Re.height=o));let l=Re.getContext("2d");if(e instanceof ImageData?l.putImageData(e,0,0):l.drawImage(e,0,0,a,s,0,0,Re==null?void 0:Re.width,Re==null?void 0:Re.height),t.filter.enabled){if((!$t||!wt||Re.width!==wt.width||(Re==null?void 0:Re.height)!==(wt==null?void 0:wt.height))&&(wt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Re==null?void 0:Re.width,Re==null?void 0:Re.height):document.createElement("canvas"),(wt==null?void 0:wt.width)!==(Re==null?void 0:Re.width)&&(wt.width=Re==null?void 0:Re.width),(wt==null?void 0:wt.height)!==(Re==null?void 0:Re.height)&&(wt.height=Re==null?void 0:Re.height),$t=_r.flags.IS_BROWSER?new fk({canvas:wt}):null),!$t)return{tensor:null,canvas:Re};$t.reset(),$t.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&$t.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&$t.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&$t.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&$t.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&$t.addFilter("hue",t.filter.hue),t.filter.negative&&$t.addFilter("negative"),t.filter.sepia&&$t.addFilter("sepia"),t.filter.vintage&&$t.addFilter("brownie"),t.filter.sepia&&$t.addFilter("sepia"),t.filter.kodachrome&&$t.addFilter("kodachrome"),t.filter.technicolor&&$t.addFilter("technicolor"),t.filter.polaroid&&$t.addFilter("polaroid"),t.filter.pixelate!==0&&$t.addFilter("pixelate",t.filter.pixelate),$t.apply(Re)}else wt=Re,$t&&($t=null);let c;if(wt.data){let h=[wt.height,wt.width,3];c=xd(wt.data,h,"int32")}else if(wt instanceof ImageData)c=pi.fromPixels(wt);else if(t.backend==="webgl"||t.backend==="humangl"){let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(wt,0,0),c=pi.fromPixels(h)}else{let h=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");h.width=i,h.height=o;let d=h.getContext("2d");d==null||d.drawImage(wt,0,0);let p=d==null?void 0:d.getImageData(0,0,i,o);c=pi.fromPixels(p)}let u=c.toFloat();n=u.expandDims(0),c.dispose(),u.dispose()}let r=t.filter.return?wt:null;return{tensor:n,canvas:r}}var Xg={};Jn(Xg,{all:()=>roe,body:()=>Ak,canvas:()=>noe,face:()=>yk,gesture:()=>mk,hand:()=>gk,object:()=>xk,options:()=>ji});var ht={backend:"webgl",modelBasePath:"../models/",wasmPath:"../assets/",debug:!0,async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface-back.json",rotation:!1,maxFaces:10,skipFrames:21,skipInitial:!1,minConfidence:.2,iouThreshold:.1,scoreThreshold:.2,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},iris:{enabled:!0,modelPath:"iris.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:31},emotion:{enabled:!0,minConfidence:.1,skipFrames:32,modelPath:"emotion.json"},age:{enabled:!1,modelPath:"age.json",skipFrames:33},gender:{enabled:!1,minConfidence:.1,modelPath:"gender.json",skipFrames:34},embedding:{enabled:!1,modelPath:"mobileface.json"}},body:{enabled:!0,modelPath:"posenet.json",maxDetections:10,scoreThreshold:.3,nmsRadius:20},hand:{enabled:!0,rotation:!1,skipFrames:12,skipInitial:!1,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"handdetect.json"},skeleton:{modelPath:"handskeleton.json"}},object:{enabled:!1,modelPath:"nanodet.json",minConfidence:.2,iouThreshold:.4,maxResults:10,skipFrames:41}};var ji={color:"rgba(173, 216, 230, 0.3)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:20,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!1,useRawBoxes:!1};function U0(e,t,n,r=0,a){e.fillStyle=a.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:a.color,e.beginPath(),e.arc(t,n,a.pointSize,0,2*Math.PI),e.fill()}function su(e,t,n,r,a,s){if(e.beginPath(),s.useCurves){let i=(t+t+r)/2,o=(n+n+a)/2;e.ellipse(i,o,r/2,a/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+r-s.roundRect,n),e.quadraticCurveTo(t+r,n,t+r,n+s.roundRect),e.lineTo(t+r,n+a-s.roundRect),e.quadraticCurveTo(t+r,n+a,t+r-s.roundRect,n+a),e.lineTo(t+s.roundRect,n+a),e.quadraticCurveTo(t,n+a,t,n+a-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function Kg(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let r of t)e.strokeStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r[2]?`rgba(${127.5+2*r[2]}, ${127.5-2*r[2]}, 255, 0.3)`:n.color,e.lineTo(r[0],parseInt(r[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function ah(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){Kg(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let r=0;r<t.length-2;r++){let a=(t[r][0]+t[r+1][0])/2,s=(t[r][1]+t[r+1][1])/2;e.quadraticCurveTo(t[r][0],t[r][1],a,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function mk(e,t,n){let r=Qn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!a)return;a.font=r.font,a.fillStyle=r.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let c=o[1]>0?`#${o[1]}`:"",u=`${o[0]} ${c}: ${l[1]}`;r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(u,8,2+s*r.lineHeight)),a.fillStyle=r.labelColor,a.fillText(u,6,0+s*r.lineHeight),s+=1}}}async function yk(e,t,n){let r=Qn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a)for(let s of t){a.font=r.font,a.strokeStyle=r.color,a.fillStyle=r.color,r.drawBoxes&&(r.useRawBoxes?su(a,e.width*s.boxRaw[0],e.height*s.boxRaw[1],e.width*s.boxRaw[2],e.height*s.boxRaw[3],r):su(a,s.box[0],s.box[1],s.box[2],s.box[3],r));let i=[];if(i.push(`face confidence: ${Math.trunc(100*s.confidence)}%`),s.genderConfidence&&i.push(`${s.gender||""} ${Math.trunc(100*s.genderConfidence)}% confident`),s.age&&i.push(`age: ${s.age||""}`),s.iris&&i.push(`iris distance: ${s.iris}`),s.emotion&&s.emotion.length>0){let o=s.emotion.map(l=>`${Math.trunc(100*l.score)}% ${l.emotion}`);i.push(o.join(" "))}s.rotation&&s.rotation.angle&&s.rotation.angle.roll&&i.push(`roll: ${Math.trunc(100*s.rotation.angle.roll)/100} yaw:${Math.trunc(100*s.rotation.angle.yaw)/100} pitch:${Math.trunc(100*s.rotation.angle.pitch)/100}`),i.length===0&&i.push("face"),a.fillStyle=r.color;for(let o=i.length-1;o>=0;o--){let l=Math.max(s.box[0],0),c=o*r.lineHeight+s.box[1];r.shadowColor&&r.shadowColor!==""&&(a.fillStyle=r.shadowColor,a.fillText(i[o],l+5,c+16)),a.fillStyle=r.labelColor,a.fillText(i[o],l+4,c+15)}if(a.lineWidth=1,s.mesh&&s.mesh.length>0){if(r.drawPoints)for(let o of s.mesh)U0(a,o[0],o[1],o[2],r);if(r.drawPolygons){a.lineWidth=1;for(let o=0;o<Bi.length/3;o++){let l=[Bi[o*3+0],Bi[o*3+1],Bi[o*3+2]].map(c=>s.mesh[c]);Kg(a,l,r)}if(s.annotations&&s.annotations.leftEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.leftEyeIris[3][0]-s.annotations.leftEyeIris[1][0])/2,l=Math.abs(s.annotations.leftEyeIris[4][1]-s.annotations.leftEyeIris[2][1])/2;a.ellipse(s.annotations.leftEyeIris[0][0],s.annotations.leftEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}if(s.annotations&&s.annotations.rightEyeIris){a.strokeStyle=r.useDepth?"rgba(255, 200, 255, 0.3)":r.color,a.beginPath();let o=Math.abs(s.annotations.rightEyeIris[3][0]-s.annotations.rightEyeIris[1][0])/2,l=Math.abs(s.annotations.rightEyeIris[4][1]-s.annotations.rightEyeIris[2][1])/2;a.ellipse(s.annotations.rightEyeIris[0][0],s.annotations.rightEyeIris[0][1],o,l,0,0,2*Math.PI),a.stroke(),r.fillPolygons&&(a.fillStyle=r.useDepth?"rgba(255, 255, 200, 0.3)":r.color,a.fill())}}}}}var ss=[];async function Ak(e,t,n){let r=Qn(ji,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let a=e.getContext("2d");if(!!a){a.lineJoin="round";for(let s=0;s<t.length;s++){if(!ss[s]&&r.bufferedOutput&&(ss[s]={...t[s]}),a.strokeStyle=r.color,a.lineWidth=r.lineWidth,r.drawPoints)for(let i=0;i<t[s].keypoints.length;i++)a.fillStyle=r.useDepth&&t[s].keypoints[i].position.z?`rgba(${127.5+2*t[s].keypoints[i].position.z}, ${127.5-2*t[s].keypoints[i].position.z}, 255, 0.5)`:r.color,r.bufferedOutput?(ss[s].keypoints[i][0]=(ss[s].keypoints[i][0]+t[s].keypoints[i].position.x)/2,ss[s].keypoints[i][1]=(ss[s].keypoints[i][1]+t[s].keypoints[i].position.y)/2,U0(a,ss[s].keypoints[i][0],ss[s].keypoints[i][1],0,r)):U0(a,t[s].keypoints[i].position.x,t[s].keypoints[i].position.y,0,r);if(r.drawLabels&&(a.font=r.font,t[s].keypoints))for(let i of t[s].keypoints)a.fillStyle=r.useDepth&&i.position.z?`rgba(${127.5+2*i.position.z}, ${127.5-2*i.position.z}, 255, 0.5)`:r.color,a.fillText(`${i.part}`,i.position.x+4,i.position.y+4);if(r.drawPolygons&&t[s].keypoints){let 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-2Q==`;var tk="1.5.1";var nu,Qc,eh,Wi,U0,th,H0,G0,q0,nk=class{constructor(t={}){nu.set(this,void 0);Qc.set(this,void 0);eh.set(this,void 0);Wi.set(this,void 0);this.analyze=(...t)=>{if(!or(this,Qc))return;let n=this.tf.engine().state.numTensors,r=or(this,nu);os(this,nu,n);let a=n-r;a!==0&&le(...t,a)};U0.set(this,t=>{if(!or(this,eh))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof We))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});th.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let r=Ye();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&le("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&le("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&le(`wasm execution: ${a?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),a||le("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&a8();try{await this.tf.setBackend(this.config.backend)}catch(a){le("error: cannot set backend:",this.config.backend,a)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(le("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let a=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&le(`gl version:${a.getParameter(a.VERSION)} renderer:${a.getParameter(a.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-r)}});H0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(V0);break;case"full":n=await t(j0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});G0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+V0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+j0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));q0.set(this,async()=>{var i;if(typeof void 0=="undefined")return this.config.debug&&le("Warmup tfjs-node not loaded"),null;let t=o=>Buffer.from(o,"base64"),n;if(this.config.warmup==="face"&&(n=t(V0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(j0)),!n)return null;let r=(i=void 0)==null?void 0:i.decodeJpeg(n),a=r.expandDims(0);this.tf.dispose(r);let s=await this.detect(a,this.config);return this.tf.dispose(a),s});this.tf=du,this.draw=Dg,this.version=tk,this.config=Jn(ht,t),this.state="idle",os(this,nu,0),os(this,Qc,!1),os(this,eh,!1),os(this,Wi,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>Fg(n,this.config),this.classes={facemesh:eg,age:R2,gender:F2,emotion:P2,faceres:U2,body:this.config.body.modelPath.includes("posenet")?dg:vg,hand:xg,nanodet:Tg},this.faceTriangulation=A8,this.faceUVMap=y8,this.sysinfo=Kg()}profileData(){return this.config.profile?m0:{}}similarity(t,n){return this.config.face.description.enabled?G2(t,n):this.config.face.embedding.enabled?s8(t,n):0}enhance(t){return q2(t)}match(t,n,r=0){return i8(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=Jn(this.config,t)),or(this,Wi)&&(this.config.debug&&le(`version: ${this.version}`),this.config.debug&&le(`tfjs version: ${this.tf.version_core}`),this.config.debug&&le("platform:",this.sysinfo.platform),this.config.debug&&le("agent:",this.sysinfo.agent),await or(this,th).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&le("configuration:",this.config),this.config.debug&&le("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?ng(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?M2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?z2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?B2(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?V2(this.config):null),this.models.handpose||(this.config.hand.enabled?_g(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?fg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?kg(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?Ng(this.config):null),this.models.nanodet||(this.config.object.enabled?Cg(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?H2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await ng(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await M2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await z2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await B2(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await V2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await _g(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await fg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await kg(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Ng(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await Cg(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await H2(this.config))),or(this,Wi)&&(this.config.debug&&le("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),os(this,Wi,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var A,y,g,w;this.state="config";let a;this.config=Jn(this.config,n),this.state="check";let s=or(this,U0).call(this,t);s&&(le(s,t),r({error:s}));let i=Ye();await or(this,th).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&Mg instanceof ImageBitmap||t instanceof We)&&(le("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),a=Ye();let l=Fg(t,this.config);if(!l||!l.tensor){le("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let c,u,h,d,p;this.config.async?(h=this.config.face.enabled?X2(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),h=this.config.face.enabled?await X2(this,l.tensor):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.face=p)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?(A=this.models.posenet)==null?void 0:A.estimatePoses(l.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?Ig(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?Sg(l.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",a=Ye(),this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?await((y=this.models.posenet)==null?void 0:y.estimatePoses(l.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?c=this.config.body.enabled?await Ig(l.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(c=this.config.body.enabled?await Sg(l.tensor,this.config):[]),p=Math.trunc(Ye()-a),p>0&&(this.perf.body=p)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(u=this.config.hand.enabled?(g=this.models.handpose)==null?void 0:g.estimateHands(l.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",a=Ye(),u=this.config.hand.enabled?await((w=this.models.handpose)==null?void 0:w.estimateHands(l.tensor,this.config)):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.hand=p)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(d=this.config.object.enabled?Rg(l.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",a=Ye(),d=this.config.object.enabled?await Rg(l.tensor,this.config):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.object=p)),this.analyze("End Object:"),this.config.async&&([h,c,u,d]=await Promise.all([h,c,u,d])),_e(l.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let f=[];this.config.gesture.enabled&&(a=Ye(),f=[...G8(h),...H8(c),...X8(u),...q8(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(Ye()-a)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(Ye()-i),this.state="idle";let m={face:h,body:c,hand:u,gesture:f,object:d,performance:this.perf,canvas:l.canvas};r(m)})}async warmup(t={}){let n=Ye();if(t&&(this.config=Jn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await or(this,H0).call(this):typeof Image!="undefined"?a=await or(this,G0).call(this):a=await or(this,q0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&le("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};nu=new WeakMap,Qc=new WeakMap,eh=new WeakMap,Wi=new WeakMap,U0=new WeakMap,th=new WeakMap,H0=new WeakMap,G0=new WeakMap,q0=new WeakMap;return vie;})();
+2Q==`;var wk="1.5.1";var iu,sh,ih,Ui,q0,oh,X0,K0,Z0,bk=class{constructor(t={}){iu.set(this,void 0);sh.set(this,void 0);ih.set(this,void 0);Ui.set(this,void 0);this.analyze=(...t)=>{if(!ur(this,sh))return;let n=this.tf.engine().state.numTensors,r=ur(this,iu);us(this,iu,n);let a=n-r;a!==0&&le(...t,a)};q0.set(this,t=>{if(!ur(this,ih))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof ze))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});oh.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let r=Ye();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&le("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&le("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let a=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&le(`wasm execution: ${a?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!a&&le("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&v8();try{await this.tf.setBackend(this.config.backend)}catch(a){le("error: cannot set backend:",this.config.backend,a)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(le("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let a=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&le(`gl version:${a.getParameter(a.VERSION)} renderer:${a.getParameter(a.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(Ye()-r)}});X0.set(this,async()=>{let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),n,r;switch(this.config.warmup){case"face":n=await t(H0);break;case"full":n=await t(G0);break;default:n=null}if(n){let a=await createImageBitmap(n);r=await this.detect(a,this.config),a.close()}return r});K0.set(this,async()=>new Promise(t=>{let n,r=0;switch(this.config.warmup){case"face":r=256,n="data:image/jpeg;base64,"+H0;break;case"full":case"body":r=1200,n="data:image/jpeg;base64,"+G0;break;default:n=null}let a=new Image;a.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(r,r):document.createElement("canvas");s.width=a.naturalWidth,s.height=a.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(a,0,0);let o=await this.detect(s,this.config);t(o)},n?a.src=n:t(null)}));Z0.set(this,async()=>{let t=a=>Buffer.from(a,"base64"),n;if(this.config.warmup==="face"&&(n=t(H0)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(G0)),!n)return null;let r;if(typeof void 0!="undefined"){let a=(void 0).decodeJpeg(n),s=a.expandDims(0);this.tf.dispose(a),r=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&le("Warmup tfjs-node not loaded");return r});this.tf=gu,this.draw=Xg,this.version=wk,this.config=Qn(ht,t),this.state="idle",us(this,iu,0),us(this,sh,!1),us(this,ih,!1),us(this,Ui,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>qg(n,this.config),this.classes={facemesh:fg,age:H2,gender:q2,emotion:J2,faceres:ag,body:this.config.body.modelPath.includes("posenet")?Sg:zg,hand:Fg,nanodet:Vg},this.faceTriangulation=D8,this.faceUVMap=O8,this.sysinfo=u5()}profileData(){return this.config.profile?g0:{}}similarity(t,n){return this.config.face.description.enabled?ig(t,n):this.config.face.embedding.enabled?k8(t,n):0}enhance(t){return og(t)}match(t,n,r=0){return I8(t,n,r)}async load(t={}){this.state="load";let n=Ye();t&&(this.config=Qn(this.config,t)),ur(this,Ui)&&(this.config.debug&&le(`version: ${this.version}`),this.config.debug&&le(`tfjs version: ${this.tf.version_core}`),this.config.debug&&le("platform:",this.sysinfo.platform),this.config.debug&&le("agent:",this.sysinfo.agent),await ur(this,oh).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&le("configuration:",this.config),this.config.debug&&le("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?yg(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?G2(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Y2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?tg(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?ng(this.config):null),this.models.handpose||(this.config.hand.enabled?Og(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?Tg(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?Pg(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?Wg(this.config):null),this.models.nanodet||(this.config.object.enabled?Ug(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?sg(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await yg(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await G2(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Y2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await tg(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await ng(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await Og(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await Tg(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await Pg(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Wg(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await Ug(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await sg(this.config))),ur(this,Ui)&&(this.config.debug&&le("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),us(this,Ui,!1));let r=Math.trunc(Ye()-n);r>(this.perf.load||0)&&(this.perf.load=r)}async detect(t,n={}){return new Promise(async r=>{var y,A,g,x;this.state="config";let a;this.config=Qn(this.config,n),this.state="check";let s=ur(this,q0).call(this,t);s&&(le(s,t),r({error:s}));let i=Ye();await ur(this,oh).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&Gg instanceof ImageBitmap||t instanceof ze)&&(le("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),a=Ye();let l=qg(t,this.config);if(!l||!l.tensor){le("could not convert input to tensor"),r({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(Ye()-a),this.analyze("Get Image:");let c,u,h,d,p;this.config.async?(h=this.config.face.enabled?lg(this,l.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",a=Ye(),h=this.config.face.enabled?await lg(this,l.tensor):[],p=Math.trunc(Ye()-a),p>0&&(this.perf.face=p)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?c=this.config.body.enabled?(y=this.models.posenet)==null?void 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m={face:h,body:c,hand:u,gesture:f,object:d,performance:this.perf,canvas:l.canvas};r(m)})}async warmup(t={}){let n=Ye();if(t&&(this.config=Qn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let r=this.config.videoOptimized;this.config.videoOptimized=!1;let a;typeof createImageBitmap=="function"?a=await ur(this,X0).call(this):typeof Image!="undefined"?a=await ur(this,K0).call(this):a=await ur(this,Z0).call(this),this.config.videoOptimized=r;let s=Ye();return this.config.debug&&le("Warmup",this.config.warmup,Math.round(s-n),"ms",a),a}};iu=new WeakMap,sh=new WeakMap,ih=new WeakMap,Ui=new WeakMap,q0=new WeakMap,oh=new WeakMap,X0=new WeakMap,K0=new WeakMap,Z0=new WeakMap;return soe;})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/human.js.map b/dist/human.js.map
index ac227424..91ad4958 100644
--- a/dist/human.js.map
+++ b/dist/human.js.map
@@ -1,7 +1,7 @@
 {
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-  "sourcesContent": ["import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore // typescript cannot infer type\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore // typescript cannot infer type\n        this.models.handpose,\n        // @ts-ignore // typescript cannot infer type\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    // @ts-ignore\n    if (typeof tf.node === 'undefined') {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      return null;\n    }\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    // @ts-ignore // tf.node is only defined when compiling for nodejs\n    const data = tf.node?.decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 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xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var 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print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"v\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"w\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"x\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"z\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var 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_RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1250, moduleBytes: 4013323, imports: 7, importBytes: 2255, outputBytes: 2991826, outputFiles: 'dist/tfjs.esm.js' }\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1253, moduleBytes: 4029357, imports: 7, importBytes: 2285, outputBytes: 2998298, outputFiles: 'dist/tfjs.esm.js' }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - compiled\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - sources\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export versions\nexport const version = {\n  tfjs: packageBundle?.version || undefined,\n  'tfjs-core': packageCore?.version || undefined,\n  'tfjs-data': packageData?.version || undefined,\n  'tfjs-layers': packageLayers?.version || undefined,\n  'tfjs-converter': packageConverter?.version || undefined,\n  'tfjs-backend-cpu': version_cpu || undefined,\n  'tfjs-backend-webgl': version_webgl || undefined,\n  'tfjs-backend-wasm': version_wasm || undefined,\n};\n// export const version = {};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 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0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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{\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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+  "sourcesContent": ["import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        this.models.handpose,\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? <any>posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    let res;\n    if (typeof tf['node'] !== 'undefined') {\n      const data = tf['node'].decodeJpeg(img);\n      const expanded = data.expandDims(0);\n      this.tf.dispose(data);\n      // log('Input:', expanded);\n      res = await this.detect(expanded, this.config);\n      this.tf.dispose(expanded);\n    } else {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      /*\n      const input = await canvasJS.loadImage(img);\n      const canvas = canvasJS.createCanvas(input.width, input.height);\n      const ctx = canvas.getContext('2d');\n      ctx.drawImage(img, 0, 0, input.width, input.height);\n      res = await this.detect(input, this.config);\n      */\n    }\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') 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xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"D\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){removeRunDependency(\"wasm-instantiate\")}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={9800:function(){throw\"Canceled!\"},9818:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";var pthread=PThread.pthreads[pthread_ptr];if(pthread){GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},runExitHandlersAndDeinitThread:function(tb,exitCode){Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0)},threadExit:function(exitCode){var tb=_pthread_self();if(tb){PThread.runExitHandlersAndDeinitThread(tb,exitCode);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlersAndDeinitThread(_pthread_self(),-1);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"v\":__emscripten_notify_thread_queue,\"b\":_abort,\"w\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"x\":_emscripten_num_logical_cores,\"s\":_emscripten_receive_on_main_thread_js,\"r\":_emscripten_resize_heap,\"t\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"u\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"q\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var 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err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"i\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"o\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}function _pthread_create(){return 6}function setErrNo(value){HEAP32[___errno_location()>>2]=value;return value}function _sysconf(name){switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write,\"g\":_pthread_create,\"h\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"m\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _All=Module[\"_All\"]=function(){return(_All=Module[\"_All\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Any=Module[\"_Any\"]=function(){return(_Any=Module[\"_Any\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"v\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"w\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"x\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"z\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _MirrorPad=Module[\"_MirrorPad\"]=function(){return(_MirrorPad=Module[\"_MirrorPad\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=function(){return(_NonMaxSuppressionV3=Module[\"_NonMaxSuppressionV3\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=function(){return(_NonMaxSuppressionV4=Module[\"_NonMaxSuppressionV4\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var 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___errno_location=Module[\"___errno_location\"]=function(){return(___errno_location=Module[\"___errno_location\"]=Module[\"asm\"][\"Pa\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Qa\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ra\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"Sa\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModule(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1250, moduleBytes: 4013323, imports: 7, importBytes: 2255, outputBytes: 2991826, outputFiles: 'dist/tfjs.esm.js' }\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1253, moduleBytes: 4029357, imports: 7, importBytes: 2285, outputBytes: 2998298, outputFiles: 'dist/tfjs.esm.js' }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - compiled\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - sources\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export versions\nexport const version = {\n  tfjs: packageBundle?.version || undefined,\n  'tfjs-core': packageCore?.version || undefined,\n  'tfjs-data': packageData?.version || undefined,\n  'tfjs-layers': packageLayers?.version || undefined,\n  'tfjs-converter': packageConverter?.version || undefined,\n  'tfjs-backend-cpu': version_cpu || undefined,\n  'tfjs-backend-webgl': version_webgl || undefined,\n  'tfjs-backend-wasm': version_wasm || undefined,\n};\n// export const version = {};\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 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0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input.toFloat(), angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 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{\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = 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diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js
index 800adb0c..a97f632a 100644
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
@@ -5,13 +5,13 @@
   author: <https://github.com/vladmandic>'
   */
 
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dn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],fn=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],mn=[33,133,362,263,1,78,308],br=dn.map(e=>Oe[e]),gr=fn.map(e=>Oe[e]),Tr=mn.map(e=>Oe[e]);var Jt=ie.leftEyeLower0,Ct=ie.rightEyeLower0,ze={leftBounds:[Jt[0],Jt[Jt.length-1]],rightBounds:[Ct[0],Ct[Ct.length-1]]},At={count:468,mouth:13,symmetryLine:[13,ie.midwayBetweenEyes[0]]},$1={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Ee={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function _t(e,t,n,A){for(let _=0;_<Yt.length;_++){let{key:c,indices:r}=Yt[_],o=ie[`${n}${c}`];if(!A||A.includes(c))for(let s=0;s<r.length;s++){let i=r[s];e[o[s]]=[t[i][0],t[i][1],(t[i][2]+e[o[s]][2])/2]}}}var Kt=class{constructor(t,n,A){var _,c;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=A,this.boxSize=((_=t==null?void 0:t.model)==null?void 0:_.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((c=t==null?void 0:t.model)==null?void 0:c.inputs[0].shape[2]),this.irisSize=(A==null?void 0:A.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,A,_){let c=Ne({startPoint:n.startPoint,endPoint:n.endPoint}),r=t.map(h=>[c[0]/this.meshSize*(h[0]-this.meshSize/2),c[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=A!==0?ct(A,[0,0]):rt,s=A!==0?r.map(h=>[...Q1(h,o),h[2]]):r,i=A!==0?G1(_):rt,a=[...Pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(h=>[h[0]+ye(a,i[0]),h[1]+ye(a,i[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ze.leftBounds[0]][2],A=t[ze.rightBounds[0]][2];return n-A}getEyeBox(t,n,A,_,c=!1){let r=nt(tt(this.calculateLandmarksBoundingBox([t[A],t[_]]),this.irisEnlarge)),o=Ne(r),s=B.image.cropAndResize(n,[[r.startPoint[1]/this.meshSize,r.startPoint[0]/this.meshSize,r.endPoint[1]/this.meshSize,r.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return c&&B.ENV.flags.IS_BROWSER&&(s=B.image.flipLeftRight(s)),{box:r,boxSize:o,crop:s}}getEyeCoords(t,n,A,_=!1){let c=[];for(let r=0;r<Ee.numCoordinates;r++){let o=t[r*3],s=t[r*3+1],i=t[r*3+2];c.push([(_?1-o/this.irisSize:o/this.irisSize)*A[0]+n.startPoint[0],s/this.irisSize*A[1]+n.startPoint[1],i])}return{rawCoords:c,iris:c.slice(Ee.index)}}getAdjustedIrisCoords(t,n,A){let _=t[ie[`${A}EyeUpper0`][Ee.upperCenter]][2],c=t[ie[`${A}EyeLower0`][Ee.lowerCenter]][2],r=(_+c)/2;return n.map((o,s)=>{let i=r;return s===2?i=_:s===4&&(i=c),[o[0],o[1],i]})}async predict(t,n){let A=!1,_;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(_=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||_&&_.boxes&&(!n.face.mesh.enabled||_.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let r of _.boxes)this.storedBoxes.push({startPoint:r.box.startPoint.dataSync(),endPoint:r.box.endPoint.dataSync(),landmarks:r.landmarks,confidence:r.confidence});this.storedBoxes.length>0&&(A=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),A){if(!_||!_.boxes||_.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let r=0;r<this.storedBoxes.length;r++){let o=C1({startPoint:this.storedBoxes[r].startPoint,endPoint:this.storedBoxes[r].endPoint},_.scaleFactor),s=tt(o),i=nt(s),a=this.storedBoxes[r].landmarks.arraySync(),h=this.storedBoxes[r].confidence;this.storedBoxes[r]={...i,confidence:h,landmarks:a}}}_&&_.boxes&&_.boxes.forEach(r=>{r.box.startPoint.dispose(),r.box.endPoint.dispose(),r.landmarks.dispose()});let c=B.tidy(()=>this.storedBoxes.map((r,o)=>{let s=r.confidence,i,a=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=At.count?At.symmetryLine:$1.symmetryLine;a=Ut(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=ct(-a,b),n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{h=rt;let u=t.clone();n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:r,faceConfidence:null,boxConfidence:s,confidence:r.confidence,image:i};let[,y,d]=this.meshDetector.predict(i),p=y.dataSync()[0];if(p<n.face.detector.minConfidence)return null;let f=B.reshape(d,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:u,boxSize:w,crop:b}=this.getEyeBox(f,i,ze.leftBounds[0],ze.leftBounds[1],!0),{box:M,boxSize:R,crop:H}=this.getEyeBox(f,i,ze.rightBounds[0],ze.rightBounds[1]),D=this.irisModel.predict(B.concat([b,H])).dataSync(),Pt=D.slice(0,Ee.numCoordinates*3),{rawCoords:Fe,iris:me}=this.getEyeCoords(Pt,u,w,!0),B0=D.slice(Ee.numCoordinates*3),{rawCoords:O1,iris:U0}=this.getEyeCoords(B0,M,R),k1=this.getLeftToRightEyeDepthDifference(f);Math.abs(k1)<30?(_t(f,Fe,"left",null),_t(f,O1,"right",null)):k1<1?_t(f,Fe,"left",["EyeUpper0","EyeLower0"]):_t(f,O1,"right",["EyeUpper0","EyeLower0"]);let Y0=this.getAdjustedIrisCoords(f,me,"left"),J0=this.getAdjustedIrisCoords(f,U0,"right");f=f.concat(Y0).concat(J0)}let m=this.transformRawCoords(f,r,a,h);r=tt(this.calculateLandmarksBoundingBox(m),1.5);let T=B.tensor2d(m);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=At.count?At.symmetryLine:$1.symmetryLine;a=Ut(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=ct(-a,b),i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let v={coords:T,box:r,faceConfidence:p,boxConfidence:s,image:i,rawCoords:f},k=nt(r);return this.storedBoxes[o]={...k,landmarks:m,confidence:r.confidence,faceConfidence:p},v}));return c=c.filter(r=>r!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(r=>r.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(t){let n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c,landmarks:t}}};var Qt=class{constructor(t,n,A,_){this.facePipeline=new Kt(t,n,A),this.config=_}async estimateFaces(t,n){let A=await this.facePipeline.predict(t,n),_=[];for(let c of A||[]){if(c.isDisposedInternal)continue;let r=c.coords?c.coords.arraySync():[],o=r.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),s={};if(r&&r.length>0)for(let h of Object.keys(ie))s[h]=ie[h].map(y=>r[y]);let i=c.box?[Math.max(0,c.box.startPoint[0]),Math.max(0,c.box.startPoint[1]),Math.min(t.shape[2],c.box.endPoint[0])-Math.max(0,c.box.startPoint[0]),Math.min(t.shape[1],c.box.endPoint[1])-Math.max(0,c.box.startPoint[1])]:0,a=c.box?[c.box.startPoint[0]/t.shape[2],c.box.startPoint[1]/t.shape[1],(c.box.endPoint[0]-c.box.startPoint[0])/t.shape[2],(c.box.endPoint[1]-c.box.startPoint[1])/t.shape[1]]:[];_.push({confidence:Math.round(100*c.faceConfidence||100*c.boxConfidence||0)/100,boxConfidence:Math.round(100*c.boxConfidence)/100,faceConfidence:Math.round(100*c.faceConfidence)/100,box:i,boxRaw:a,mesh:r,meshRaw:o,annotations:s,image:c.image?c.image.clone():null}),c.coords&&c.coords.dispose(),c.image&&c.image.dispose()}return _}},X=[null,null,null];async function $t(e){return!X[0]&&e.face.enabled||!X[1]&&e.face.mesh.enabled||!X[2]&&e.face.iris.enabled?(X=await Promise.all([!X[0]&&e.face.enabled?Y1(e):null,!X[1]&&e.face.mesh.enabled?Gt.loadGraphModel(W(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!X[2]&&e.face.iris.enabled?Gt.loadGraphModel(W(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!X[1]||!X[1].modelUrl?l("load model failed:",e.face.mesh.modelPath):e.debug&&l("load model:",X[1].modelUrl)),e.face.iris.enabled&&(!X[2]||!X[1].modelUrl?l("load model failed:",e.face.iris.modelPath):e.debug&&l("load model:",X[2].modelUrl))):e.debug&&(l("cached model:",X[0].model.modelUrl),l("cached model:",X[1].modelUrl),l("cached model:",X[2].modelUrl)),new Qt(X[0],X[1],X[2],e)}var e0=ue,t0=Oe;var h1={};ce(h1,{PoseNet:()=>i1,load:()=>a1});var p0=L(I());var n0=L(I());function pn(e){let[t,n,A,_]=e;return{offsets:t,heatmap:n,displacementFwd:A,displacementBwd:_}}var e1=class{constructor(t){this.model=t}predict(t){return n0.tidy(()=>{let A=t.toFloat().div(127.5).sub(1).expandDims(0),c=this.model.predict(A).map(o=>o.squeeze([0])),r=pn(c);return{heatmapScores:r.heatmap.sigmoid(),offsets:r.offsets,displacementFwd:r.displacementFwd,displacementBwd:r.displacementBwd}})}dispose(){this.model.dispose()}};function t1(e){return Math.floor(e/2)}var n1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(t1(t),t);)this.exchange(t,t1(t)),t=t1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=A}};function un(e,t,n,A,_,c){let[r,o]=c.shape,s=!0,i=Math.max(n-_,0),a=Math.min(n+_+1,r);for(let h=i;h<a;++h){let y=Math.max(A-_,0),d=Math.min(A+_+1,o);for(let p=y;p<d;++p)if(c.get(h,p,e)>t){s=!1;break}if(!s)break}return s}function r0(e,t,n){let[A,_,c]=n.shape,r=new n1(A*_*c,({score:o})=>o);for(let o=0;o<A;++o)for(let s=0;s<_;++s)for(let i=0;i<c;++i){let a=n.get(o,s,i);a<e||un(i,a,o,s,t,n)&&r.enqueue({score:a,part:{heatmapY:o,heatmapX:s,id:i}})}return r}var ve=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Re=ve.length,ke=ve.reduce((e,t,n)=>(e[t]=n,e),{}),bn=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Lr=bn.map(([e,t])=>[ke[e],ke[t]]),c0=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function r1(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function ot(e,t,n){let{heatmapY:A,heatmapX:_,id:c}=e,{y:r,x:o}=r1(A,_,c,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+r}}function c1(e,t,n){return e<t?t:e>n?n:e}function A0(e,t,n,A){let _=n-e,c=A-t;return _*_+c*c}function A1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var ne=L(I());function _0(e,t){let n=t.shape[0],A=new Float32Array(n);for(let _=0;_<n;_++){let c=t.get(_,0),r=t.get(_,1);A[_]=e.get(c,r,_)}return A}function gn(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function Tn(e,t){let n=[];for(let A=0;A<Re;A++){let _=e.get(A,0).valueOf(),c=e.get(A,1).valueOf(),{x:r,y:o}=gn(_,c,A,t);n.push(o),n.push(r)}return ne.tensor2d(n,[Re,2])}function o0(e,t,n){return ne.tidy(()=>e.toTensor().mul(ne.scalar(t,"int32")).toFloat().add(Tn(e,n)))}function Pn(e,t){return ne.tidy(()=>{let n=e.div(ne.scalar(t,"int32"));return e.sub(n.mul(ne.scalar(t,"int32")))})}function s0(e){let[t,n,A]=e.shape;return ne.tidy(()=>{let c=e.reshape([t*n,A]).argMax(0),r=c.div(ne.scalar(n,"int32")).expandDims(1),o=Pn(c,n).expandDims(1);return ne.concat([r,o],1)})}var h0=c0.map(([e,t])=>[ke[e],ke[t]]),_1=h0.map(([,e])=>e),i0=h0.map(([e])=>e),Mn=16;function zn(e,t,n){let A=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,A+e)}}function o1(e,t,n,A){return{y:c1(Math.round(e.y/t),0,n-1),x:c1(Math.round(e.x/t),0,A-1)}}function a0(e,t,n,A,_,c,r,o=2){let[s,i]=A.shape,a=o1(t.position,c,s,i),h=zn(e,a,r),d=A1(t.position,h);for(let f=0;f<o;f++){let m=o1(d,c,s,i),T=r1(m.y,m.x,n,_);d=A1({x:m.x*c,y:m.y*c},{x:T.x,y:T.y})}let p=o1(d,c,s,i),x=A.get(p.y,p.x,n);return{position:d,part:ve[n],score:x}}function x0(e,t,n,A,_,c){let r=t.shape[2],o=_1.length,s=new Array(r),{part:i,score:a}=e,h=ot(i,A,n);s[i.id]={score:a,part:ve[i.id],position:h};for(let y=o-1;y>=0;--y){let d=_1[y],p=i0[y];s[d]&&!s[p]&&(s[p]=a0(y,s[d],p,t,n,A,c))}for(let y=0;y<o;++y){let d=i0[y],p=_1[y];s[d]&&!s[p]&&(s[p]=a0(y,s[d],p,t,n,A,_))}return s}async function y0(e,t,n){let A=0,_=s0(e),c=await Promise.all([e.buffer(),t.buffer(),_.buffer()]),r=c[0],o=c[1],s=c[2],i=o0(s,Mn,o),a=await i.buffer(),y=Array.from(_0(r,s)).map((p,x)=>(A+=p,{position:{y:a.get(x,0),x:a.get(x,1)},part:ve[x],score:p})),d=y.filter(p=>p.score>n);return _.dispose(),i.dispose(),{keypoints:d,score:A/y.length}}var En=1,w0=16;function l0(e,t,{x:n,y:A},_){return e.some(({keypoints:c})=>{let r=c[_].position;return A0(A,n,r.y,r.x)<=t})}function vn(e,t,n){return n.reduce((_,{position:c,score:r},o)=>(l0(e,t,c,o)||(_+=r),_),0)/n.length}function d0(e,t,n,A,_,c,r){let o=[],s=r0(r,En,e),i=_^2;for(;o.length<c&&!s.empty();){let a=s.dequeue(),h=ot(a.part,w0,t);if(l0(o,i,h,a.part.id))continue;let y=x0(a,e,t,w0,n,A),d=vn(o,i,y);d>r&&o.push({keypoints:y,score:Math.round(100*d)/100})}return o}async function f0(e){return Promise.all(e.map(t=>t.buffer()))}function Rn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:A,part:_,position:c})=>({score:A,part:_,position:{x:Math.trunc(c.x*n),y:Math.trunc(c.y*t)}}))}}function m0(e,[t,n]){let A=e.squeeze(0),_=A.resizeBilinear([t,n]);return A.dispose(),_}function s1(e,[t,n],[A,_]){return e.map(r=>Rn(r,t/A,n/_))}var be;async function Sn(e,t,n,A){return new Promise(async _=>{let c=await f0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),r=c[0],o=c[1],s=c[2],i=c[3],a=await d0(r,o,s,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=s1(a,[e.shape[1],e.shape[2]],[A,A]);_(h)})}async function jn(e,t,n,A){return new Promise(async _=>{let c=await y0(t.heatmapScores,t.offsets,n.body.scoreThreshold),r=s1([c],[e.shape[1],e.shape[2]],[A,A]);_(r)})}var i1=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let A=m0(t,[this.inputSize,this.inputSize]),_=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await jn(t,_,n,this.inputSize):await Sn(t,_,n,this.inputSize);return _.heatmapScores.dispose(),_.offsets.dispose(),_.displacementFwd.dispose(),_.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function a1(e){be?e.debug&&l("cached model:",be.modelUrl):(be=await p0.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),!be||!be.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",be.modelUrl));let t=new e1(be);return new i1(t)}var d1={};ce(d1,{HandPose:()=>p1,load:()=>u1});var f1=L(I());var P=L(I());var u0=L(I());function st(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ie(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function b0(e,t,n){let 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n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c}}};var 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S0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],j0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var J;async function g1(e){return J?e.debug&&l("cached model:",J.modelUrl):(J=await le.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),J.width=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[2].size),J.height=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[1].size),!J||!J.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",J.modelUrl)),J}async function T1(e,t){if(!J||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},A=le.image.resizeBilinear(e,[J.width,J.height],!1),_=le.div(A,[255]);A.dispose();let c;if(t.profile){let a=await le.profile(()=>J.predict(_));c=a.result.find(h=>h.size===195||h.size===155).dataSync(),a.result.forEach(h=>h.dispose()),Y("blazepose",a)}else{let a=await J.predict(_);c=a.find(h=>h.size===195||h.size===155).dataSync(),a.forEach(h=>h.dispose())}_.dispose();let r=[],o=c.length===195?S0:j0,s=5;for(let 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dn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],fn=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],mn=[33,133,362,263,1,78,308],br=dn.map(e=>Oe[e]),gr=fn.map(e=>Oe[e]),Tr=mn.map(e=>Oe[e]);var Jt=ie.leftEyeLower0,Ct=ie.rightEyeLower0,ze={leftBounds:[Jt[0],Jt[Jt.length-1]],rightBounds:[Ct[0],Ct[Ct.length-1]]},At={count:468,mouth:13,symmetryLine:[13,ie.midwayBetweenEyes[0]]},$1={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ve={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function _t(e,t,n,A){for(let _=0;_<Yt.length;_++){let{key:c,indices:r}=Yt[_],o=ie[`${n}${c}`];if(!A||A.includes(c))for(let s=0;s<r.length;s++){let i=r[s];e[o[s]]=[t[i][0],t[i][1],(t[i][2]+e[o[s]][2])/2]}}}var Kt=class{constructor(t,n,A){var _,c;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=A,this.boxSize=((_=t==null?void 0:t.model)==null?void 0:_.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((c=t==null?void 0:t.model)==null?void 0:c.inputs[0].shape[2]),this.irisSize=(A==null?void 0:A.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,A,_){let c=Ne({startPoint:n.startPoint,endPoint:n.endPoint}),r=t.map(h=>[c[0]/this.meshSize*(h[0]-this.meshSize/2),c[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=A!==0?ct(A,[0,0]):rt,s=A!==0?r.map(h=>[...Q1(h,o),h[2]]):r,i=A!==0?G1(_):rt,a=[...Pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(h=>[h[0]+ye(a,i[0]),h[1]+ye(a,i[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ze.leftBounds[0]][2],A=t[ze.rightBounds[0]][2];return n-A}getEyeBox(t,n,A,_,c=!1){let r=nt(tt(this.calculateLandmarksBoundingBox([t[A],t[_]]),this.irisEnlarge)),o=Ne(r),s=B.image.cropAndResize(n,[[r.startPoint[1]/this.meshSize,r.startPoint[0]/this.meshSize,r.endPoint[1]/this.meshSize,r.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return c&&B.ENV.flags.IS_BROWSER&&(s=B.image.flipLeftRight(s)),{box:r,boxSize:o,crop:s}}getEyeCoords(t,n,A,_=!1){let c=[];for(let r=0;r<ve.numCoordinates;r++){let o=t[r*3],s=t[r*3+1],i=t[r*3+2];c.push([(_?1-o/this.irisSize:o/this.irisSize)*A[0]+n.startPoint[0],s/this.irisSize*A[1]+n.startPoint[1],i])}return{rawCoords:c,iris:c.slice(ve.index)}}getAdjustedIrisCoords(t,n,A){let _=t[ie[`${A}EyeUpper0`][ve.upperCenter]][2],c=t[ie[`${A}EyeLower0`][ve.lowerCenter]][2],r=(_+c)/2;return n.map((o,s)=>{let i=r;return s===2?i=_:s===4&&(i=c),[o[0],o[1],i]})}async predict(t,n){let A=!1,_;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(_=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||_&&_.boxes&&(!n.face.mesh.enabled||_.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let r of _.boxes)this.storedBoxes.push({startPoint:r.box.startPoint.dataSync(),endPoint:r.box.endPoint.dataSync(),landmarks:r.landmarks,confidence:r.confidence});this.storedBoxes.length>0&&(A=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),A){if(!_||!_.boxes||_.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let r=0;r<this.storedBoxes.length;r++){let o=C1({startPoint:this.storedBoxes[r].startPoint,endPoint:this.storedBoxes[r].endPoint},_.scaleFactor),s=tt(o),i=nt(s),a=this.storedBoxes[r].landmarks.arraySync(),h=this.storedBoxes[r].confidence;this.storedBoxes[r]={...i,confidence:h,landmarks:a}}}_&&_.boxes&&_.boxes.forEach(r=>{r.box.startPoint.dispose(),r.box.endPoint.dispose(),r.landmarks.dispose()});let c=B.tidy(()=>this.storedBoxes.map((r,o)=>{let s=r.confidence,i,a=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=At.count?At.symmetryLine:$1.symmetryLine;a=Ut(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=ct(-a,b),n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{h=rt;let u=t.clone();n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:r,faceConfidence:null,boxConfidence:s,confidence:r.confidence,image:i};let[,y,d]=this.meshDetector.predict(i),p=y.dataSync()[0];if(p<n.face.detector.minConfidence)return null;let f=B.reshape(d,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:u,boxSize:w,crop:b}=this.getEyeBox(f,i,ze.leftBounds[0],ze.leftBounds[1],!0),{box:M,boxSize:R,crop:H}=this.getEyeBox(f,i,ze.rightBounds[0],ze.rightBounds[1]),D=this.irisModel.predict(B.concat([b,H])).dataSync(),Pt=D.slice(0,ve.numCoordinates*3),{rawCoords:Fe,iris:me}=this.getEyeCoords(Pt,u,w,!0),B0=D.slice(ve.numCoordinates*3),{rawCoords:O1,iris:U0}=this.getEyeCoords(B0,M,R),k1=this.getLeftToRightEyeDepthDifference(f);Math.abs(k1)<30?(_t(f,Fe,"left",null),_t(f,O1,"right",null)):k1<1?_t(f,Fe,"left",["EyeUpper0","EyeLower0"]):_t(f,O1,"right",["EyeUpper0","EyeLower0"]);let Y0=this.getAdjustedIrisCoords(f,me,"left"),J0=this.getAdjustedIrisCoords(f,U0,"right");f=f.concat(Y0).concat(J0)}let m=this.transformRawCoords(f,r,a,h);r=tt(this.calculateLandmarksBoundingBox(m),1.5);let T=B.tensor2d(m);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=At.count?At.symmetryLine:$1.symmetryLine;a=Ut(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t.toFloat(),a,0,M);h=ct(-a,b),i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let E={coords:T,box:r,faceConfidence:p,boxConfidence:s,image:i,rawCoords:f},k=nt(r);return this.storedBoxes[o]={...k,landmarks:m,confidence:r.confidence,faceConfidence:p},E}));return c=c.filter(r=>r!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(r=>r.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(t){let n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c,landmarks:t}}};var Qt=class{constructor(t,n,A,_){this.facePipeline=new Kt(t,n,A),this.config=_}async estimateFaces(t,n){let A=await this.facePipeline.predict(t,n),_=[];for(let c of A||[]){if(c.isDisposedInternal)continue;let r=c.coords?c.coords.arraySync():[],o=r.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),s={};if(r&&r.length>0)for(let h of Object.keys(ie))s[h]=ie[h].map(y=>r[y]);let i=c.box?[Math.max(0,c.box.startPoint[0]),Math.max(0,c.box.startPoint[1]),Math.min(t.shape[2],c.box.endPoint[0])-Math.max(0,c.box.startPoint[0]),Math.min(t.shape[1],c.box.endPoint[1])-Math.max(0,c.box.startPoint[1])]:0,a=c.box?[c.box.startPoint[0]/t.shape[2],c.box.startPoint[1]/t.shape[1],(c.box.endPoint[0]-c.box.startPoint[0])/t.shape[2],(c.box.endPoint[1]-c.box.startPoint[1])/t.shape[1]]:[];_.push({confidence:Math.round(100*c.faceConfidence||100*c.boxConfidence||0)/100,boxConfidence:Math.round(100*c.boxConfidence)/100,faceConfidence:Math.round(100*c.faceConfidence)/100,box:i,boxRaw:a,mesh:r,meshRaw:o,annotations:s,image:c.image?c.image.clone():null}),c.coords&&c.coords.dispose(),c.image&&c.image.dispose()}return _}},X=[null,null,null];async function $t(e){return!X[0]&&e.face.enabled||!X[1]&&e.face.mesh.enabled||!X[2]&&e.face.iris.enabled?(X=await Promise.all([!X[0]&&e.face.enabled?Y1(e):null,!X[1]&&e.face.mesh.enabled?Gt.loadGraphModel(W(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!X[2]&&e.face.iris.enabled?Gt.loadGraphModel(W(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!X[1]||!X[1].modelUrl?l("load model failed:",e.face.mesh.modelPath):e.debug&&l("load model:",X[1].modelUrl)),e.face.iris.enabled&&(!X[2]||!X[1].modelUrl?l("load model failed:",e.face.iris.modelPath):e.debug&&l("load model:",X[2].modelUrl))):e.debug&&(l("cached model:",X[0].model.modelUrl),l("cached model:",X[1].modelUrl),l("cached model:",X[2].modelUrl)),new Qt(X[0],X[1],X[2],e)}var e0=ue,t0=Oe;var h1={};ce(h1,{PoseNet:()=>i1,load:()=>a1});var p0=L(I());var n0=L(I());function pn(e){let[t,n,A,_]=e;return{offsets:t,heatmap:n,displacementFwd:A,displacementBwd:_}}var e1=class{constructor(t){this.model=t}predict(t){return n0.tidy(()=>{let A=t.toFloat().div(127.5).sub(1).expandDims(0),c=this.model.predict(A).map(o=>o.squeeze([0])),r=pn(c);return{heatmapScores:r.heatmap.sigmoid(),offsets:r.offsets,displacementFwd:r.displacementFwd,displacementBwd:r.displacementBwd}})}dispose(){this.model.dispose()}};function t1(e){return Math.floor(e/2)}var n1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(t1(t),t);)this.exchange(t,t1(t)),t=t1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=A}};function un(e,t,n,A,_,c){let[r,o]=c.shape,s=!0,i=Math.max(n-_,0),a=Math.min(n+_+1,r);for(let h=i;h<a;++h){let y=Math.max(A-_,0),d=Math.min(A+_+1,o);for(let p=y;p<d;++p)if(c.get(h,p,e)>t){s=!1;break}if(!s)break}return s}function r0(e,t,n){let[A,_,c]=n.shape,r=new n1(A*_*c,({score:o})=>o);for(let o=0;o<A;++o)for(let s=0;s<_;++s)for(let i=0;i<c;++i){let a=n.get(o,s,i);a<e||un(i,a,o,s,t,n)&&r.enqueue({score:a,part:{heatmapY:o,heatmapX:s,id:i}})}return r}var Ee=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Re=Ee.length,ke=Ee.reduce((e,t,n)=>(e[t]=n,e),{}),bn=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Lr=bn.map(([e,t])=>[ke[e],ke[t]]),c0=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function r1(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function ot(e,t,n){let{heatmapY:A,heatmapX:_,id:c}=e,{y:r,x:o}=r1(A,_,c,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+r}}function c1(e,t,n){return e<t?t:e>n?n:e}function A0(e,t,n,A){let _=n-e,c=A-t;return _*_+c*c}function A1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var ne=L(I());function _0(e,t){let n=t.shape[0],A=new Float32Array(n);for(let _=0;_<n;_++){let c=t.get(_,0),r=t.get(_,1);A[_]=e.get(c,r,_)}return A}function gn(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function Tn(e,t){let n=[];for(let A=0;A<Re;A++){let _=e.get(A,0).valueOf(),c=e.get(A,1).valueOf(),{x:r,y:o}=gn(_,c,A,t);n.push(o),n.push(r)}return ne.tensor2d(n,[Re,2])}function o0(e,t,n){return ne.tidy(()=>e.toTensor().mul(ne.scalar(t,"int32")).toFloat().add(Tn(e,n)))}function Pn(e,t){return ne.tidy(()=>{let n=e.div(ne.scalar(t,"int32"));return e.sub(n.mul(ne.scalar(t,"int32")))})}function s0(e){let[t,n,A]=e.shape;return ne.tidy(()=>{let c=e.reshape([t*n,A]).argMax(0),r=c.div(ne.scalar(n,"int32")).expandDims(1),o=Pn(c,n).expandDims(1);return ne.concat([r,o],1)})}var h0=c0.map(([e,t])=>[ke[e],ke[t]]),_1=h0.map(([,e])=>e),i0=h0.map(([e])=>e),Mn=16;function zn(e,t,n){let A=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,A+e)}}function o1(e,t,n,A){return{y:c1(Math.round(e.y/t),0,n-1),x:c1(Math.round(e.x/t),0,A-1)}}function a0(e,t,n,A,_,c,r,o=2){let[s,i]=A.shape,a=o1(t.position,c,s,i),h=zn(e,a,r),d=A1(t.position,h);for(let f=0;f<o;f++){let m=o1(d,c,s,i),T=r1(m.y,m.x,n,_);d=A1({x:m.x*c,y:m.y*c},{x:T.x,y:T.y})}let p=o1(d,c,s,i),x=A.get(p.y,p.x,n);return{position:d,part:Ee[n],score:x}}function x0(e,t,n,A,_,c){let r=t.shape[2],o=_1.length,s=new Array(r),{part:i,score:a}=e,h=ot(i,A,n);s[i.id]={score:a,part:Ee[i.id],position:h};for(let y=o-1;y>=0;--y){let d=_1[y],p=i0[y];s[d]&&!s[p]&&(s[p]=a0(y,s[d],p,t,n,A,c))}for(let y=0;y<o;++y){let d=i0[y],p=_1[y];s[d]&&!s[p]&&(s[p]=a0(y,s[d],p,t,n,A,_))}return s}async function y0(e,t,n){let A=0,_=s0(e),c=await Promise.all([e.buffer(),t.buffer(),_.buffer()]),r=c[0],o=c[1],s=c[2],i=o0(s,Mn,o),a=await i.buffer(),y=Array.from(_0(r,s)).map((p,x)=>(A+=p,{position:{y:a.get(x,0),x:a.get(x,1)},part:Ee[x],score:p})),d=y.filter(p=>p.score>n);return _.dispose(),i.dispose(),{keypoints:d,score:A/y.length}}var vn=1,w0=16;function l0(e,t,{x:n,y:A},_){return e.some(({keypoints:c})=>{let r=c[_].position;return A0(A,n,r.y,r.x)<=t})}function En(e,t,n){return n.reduce((_,{position:c,score:r},o)=>(l0(e,t,c,o)||(_+=r),_),0)/n.length}function d0(e,t,n,A,_,c,r){let o=[],s=r0(r,vn,e),i=_^2;for(;o.length<c&&!s.empty();){let a=s.dequeue(),h=ot(a.part,w0,t);if(l0(o,i,h,a.part.id))continue;let y=x0(a,e,t,w0,n,A),d=En(o,i,y);d>r&&o.push({keypoints:y,score:Math.round(100*d)/100})}return o}async function f0(e){return Promise.all(e.map(t=>t.buffer()))}function Rn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:A,part:_,position:c})=>({score:A,part:_,position:{x:Math.trunc(c.x*n),y:Math.trunc(c.y*t)}}))}}function m0(e,[t,n]){let A=e.squeeze(0),_=A.resizeBilinear([t,n]);return A.dispose(),_}function s1(e,[t,n],[A,_]){return e.map(r=>Rn(r,t/A,n/_))}var be;async function Sn(e,t,n,A){return new Promise(async _=>{let c=await f0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),r=c[0],o=c[1],s=c[2],i=c[3],a=await d0(r,o,s,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=s1(a,[e.shape[1],e.shape[2]],[A,A]);_(h)})}async function jn(e,t,n,A){return new Promise(async _=>{let c=await y0(t.heatmapScores,t.offsets,n.body.scoreThreshold),r=s1([c],[e.shape[1],e.shape[2]],[A,A]);_(r)})}var i1=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let A=m0(t,[this.inputSize,this.inputSize]),_=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await jn(t,_,n,this.inputSize):await Sn(t,_,n,this.inputSize);return _.heatmapScores.dispose(),_.offsets.dispose(),_.displacementFwd.dispose(),_.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function a1(e){be?e.debug&&l("cached model:",be.modelUrl):(be=await p0.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),!be||!be.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",be.modelUrl));let t=new e1(be);return new i1(t)}var d1={};ce(d1,{HandPose:()=>p1,load:()=>u1});var f1=L(I());var P=L(I());var u0=L(I());function st(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ie(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function b0(e,t,n){let 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n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c}}};var 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S0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],j0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var J;async function g1(e){return J?e.debug&&l("cached model:",J.modelUrl):(J=await le.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),J.width=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[2].size),J.height=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[1].size),!J||!J.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",J.modelUrl)),J}async function T1(e,t){if(!J||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},A=le.image.resizeBilinear(e,[J.width,J.height],!1),_=le.div(A,[255]);A.dispose();let c;if(t.profile){let a=await le.profile(()=>J.predict(_));c=a.result.find(h=>h.size===195||h.size===155).dataSync(),a.result.forEach(h=>h.dispose()),Y("blazepose",a)}else{let a=await J.predict(_);c=a.find(h=>h.size===195||h.size===155).dataSync(),a.forEach(h=>h.dispose())}_.dispose();let r=[],o=c.length===195?S0:j0,s=5;for(let 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cannot set backend:",this.config.backend,_)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(l("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let _=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&l(`gl version:${_.getParameter(_.VERSION)} renderer:${_.getParameter(_.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(E()-A)}});bt.set(this,async()=>{let t=(_,c="application/octet-stream")=>fetch(`data:${c};base64,${_}`).then(r=>r.blob()),n,A;switch(this.config.warmup){case"face":n=await t(mt);break;case"full":n=await t(pt);break;default:n=null}if(n){let _=await createImageBitmap(n);A=await this.detect(_,this.config),_.close()}return A});gt.set(this,async()=>new Promise(t=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+mt;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+pt;break;default:n=null}let _=new Image;_.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=_.naturalWidth,c.height=_.naturalHeight;let r=c.getContext("2d");r==null||r.drawImage(_,0,0);let o=await this.detect(c,this.config);t(o)},n?_.src=n:t(null)}));Tt.set(this,async()=>{var r;if(typeof je.node=="undefined")return this.config.debug&&l("Warmup tfjs-node not loaded"),null;let t=o=>Buffer.from(o,"base64"),n;if(this.config.warmup==="face"&&(n=t(mt)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(pt)),!n)return null;let A=(r=je.node)==null?void 0:r.decodeJpeg(n),_=A.expandDims(0);this.tf.dispose(A);let c=await this.detect(_,this.config);return this.tf.dispose(_),c});this.tf=je,this.draw=W1,this.version=F0,this.config=ee(j,t),this.state="idle",pe(this,We,0),pe(this,He,!1),pe(this,Ve,!1),pe(this,Te,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>j1(n,this.config),this.classes={facemesh:Dt,age:Et,gender:Rt,emotion:Ot,faceres:Vt,body:this.config.body.modelPath.includes("posenet")?h1:b1,hand:d1,nanodet:z1},this.faceTriangulation=e0,this.faceUVMap=t0,this.sysinfo=Z1()}profileData(){return this.config.profile?Be:{}}similarity(t,n){return this.config.face.description.enabled?Ft(t,n):this.config.face.embedding.enabled?F1(t,n):0}enhance(t){return qt(t)}match(t,n,A=0){return q1(t,n,A)}async load(t={}){this.state="load";let n=E();t&&(this.config=ee(this.config,t)),Ae(this,Te)&&(this.config.debug&&l(`version: ${this.version}`),this.config.debug&&l(`tfjs version: ${this.tf.version_core}`),this.config.debug&&l("platform:",this.sysinfo.platform),this.config.debug&&l("agent:",this.sysinfo.agent),await Ae(this,Xe).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&l("configuration:",this.config),this.config.debug&&l("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?$t(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?vt(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Nt(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Lt(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Zt(this.config):null),this.models.handpose||(this.config.hand.enabled?u1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?a1(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?g1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?P1(this.config):null),this.models.nanodet||(this.config.object.enabled?v1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Xt(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await $t(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await vt(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Nt(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Lt(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Zt(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await u1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await a1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await g1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await P1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await v1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Xt(this.config))),Ae(this,Te)&&(this.config.debug&&l("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),pe(this,Te,!1));let A=Math.trunc(E()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async detect(t,n={}){return new Promise(async A=>{var f,m,T,v;this.state="config";let _;this.config=ee(this.config,n),this.state="check";let c=Ae(this,ut).call(this,t);c&&(l(c,t),A({error:c}));let r=E();await Ae(this,Xe).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&S1 instanceof ImageBitmap||t instanceof je.Tensor)&&(l("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),_=E();let s=j1(t,this.config);if(!s||!s.tensor){l("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(E()-_),this.analyze("Get Image:");let i,a,h,y,d;this.config.async?(h=this.config.face.enabled?Bt(this,s.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",_=E(),h=this.config.face.enabled?await Bt(this,s.tensor):[],d=Math.trunc(E()-_),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?(f=this.models.posenet)==null?void 0:f.estimatePoses(s.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?T1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?M1(s.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",_=E(),this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?await((m=this.models.posenet)==null?void 0:m.estimatePoses(s.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?await T1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?await M1(s.tensor,this.config):[]),d=Math.trunc(E()-_),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(a=this.config.hand.enabled?(T=this.models.handpose)==null?void 0:T.estimateHands(s.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",_=E(),a=this.config.hand.enabled?await((v=this.models.handpose)==null?void 0:v.estimateHands(s.tensor,this.config)):[],d=Math.trunc(E()-_),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(y=this.config.object.enabled?R1(s.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",_=E(),y=this.config.object.enabled?await R1(s.tensor,this.config):[],d=Math.trunc(E()-_),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([h,i,a,y]=await Promise.all([h,i,a,y])),je.dispose(s.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(_=E(),p=[...N0(h),...W0(i),...k0(a),...O0(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(E()-_)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(E()-r),this.state="idle";let x={face:h,body:i,hand:a,gesture:p,object:y,performance:this.perf,canvas:s.canvas};A(x)})}async warmup(t={}){let n=E();if(t&&(this.config=ee(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let A=this.config.videoOptimized;this.config.videoOptimized=!1;let _;typeof createImageBitmap=="function"?_=await Ae(this,bt).call(this):typeof Image!="undefined"?_=await Ae(this,gt).call(this):_=await Ae(this,Tt).call(this),this.config.videoOptimized=A;let c=E();return this.config.debug&&l("Warmup",this.config.warmup,Math.round(c-n),"ms",_),_}};We=new WeakMap,He=new WeakMap,Ve=new WeakMap,Te=new WeakMap,ut=new WeakMap,Xe=new WeakMap,bt=new WeakMap,gt=new WeakMap,Tt=new WeakMap;0&&(module.exports={Human});
+2Q==`;var F0="1.5.1";var We,He,Ve,Te,ut,Xe,bt,gt,Tt,q0=class{constructor(t={}){We.set(this,void 0);He.set(this,void 0);Ve.set(this,void 0);Te.set(this,void 0);this.analyze=(...t)=>{if(!Ae(this,He))return;let n=this.tf.engine().state.numTensors,A=Ae(this,We);pe(this,We,n);let _=n-A;_!==0&&l(...t,_)};ut.set(this,t=>{if(!Ae(this,Ve))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof je.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Xe.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let A=v();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&l("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&l("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let _=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),c=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&l(`wasm execution: ${_?"SIMD":"no SIMD"} ${c?"multithreaded":"singlethreaded"}`),this.config.debug&&!_&&l("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&X1();try{await this.tf.setBackend(this.config.backend)}catch(_){l("error: cannot set backend:",this.config.backend,_)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(l("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let _=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&l(`gl version:${_.getParameter(_.VERSION)} renderer:${_.getParameter(_.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(v()-A)}});bt.set(this,async()=>{let t=(_,c="application/octet-stream")=>fetch(`data:${c};base64,${_}`).then(r=>r.blob()),n,A;switch(this.config.warmup){case"face":n=await t(mt);break;case"full":n=await t(pt);break;default:n=null}if(n){let _=await createImageBitmap(n);A=await this.detect(_,this.config),_.close()}return A});gt.set(this,async()=>new Promise(t=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+mt;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+pt;break;default:n=null}let _=new Image;_.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=_.naturalWidth,c.height=_.naturalHeight;let r=c.getContext("2d");r==null||r.drawImage(_,0,0);let o=await this.detect(c,this.config);t(o)},n?_.src=n:t(null)}));Tt.set(this,async()=>{let t=_=>Buffer.from(_,"base64"),n;if(this.config.warmup==="face"&&(n=t(mt)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(pt)),!n)return null;let A;if(typeof je.node!="undefined"){let _=je.node.decodeJpeg(n),c=_.expandDims(0);this.tf.dispose(_),A=await this.detect(c,this.config),this.tf.dispose(c)}else this.config.debug&&l("Warmup tfjs-node not loaded");return A});this.tf=je,this.draw=W1,this.version=F0,this.config=ee(j,t),this.state="idle",pe(this,We,0),pe(this,He,!1),pe(this,Ve,!1),pe(this,Te,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>j1(n,this.config),this.classes={facemesh:Dt,age:vt,gender:Rt,emotion:Ot,faceres:Vt,body:this.config.body.modelPath.includes("posenet")?h1:b1,hand:d1,nanodet:z1},this.faceTriangulation=e0,this.faceUVMap=t0,this.sysinfo=Z1()}profileData(){return this.config.profile?Be:{}}similarity(t,n){return this.config.face.description.enabled?Ft(t,n):this.config.face.embedding.enabled?F1(t,n):0}enhance(t){return qt(t)}match(t,n,A=0){return q1(t,n,A)}async load(t={}){this.state="load";let n=v();t&&(this.config=ee(this.config,t)),Ae(this,Te)&&(this.config.debug&&l(`version: ${this.version}`),this.config.debug&&l(`tfjs version: ${this.tf.version_core}`),this.config.debug&&l("platform:",this.sysinfo.platform),this.config.debug&&l("agent:",this.sysinfo.agent),await Ae(this,Xe).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&l("configuration:",this.config),this.config.debug&&l("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?$t(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Et(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Nt(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Lt(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Zt(this.config):null),this.models.handpose||(this.config.hand.enabled?u1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?a1(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?g1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?P1(this.config):null),this.models.nanodet||(this.config.object.enabled?E1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Xt(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await $t(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Et(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Nt(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Lt(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Zt(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await u1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await a1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await g1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await P1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await E1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Xt(this.config))),Ae(this,Te)&&(this.config.debug&&l("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),pe(this,Te,!1));let A=Math.trunc(v()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async detect(t,n={}){return new Promise(async A=>{var f,m,T,E;this.state="config";let _;this.config=ee(this.config,n),this.state="check";let c=Ae(this,ut).call(this,t);c&&(l(c,t),A({error:c}));let r=v();await Ae(this,Xe).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&S1 instanceof ImageBitmap||t instanceof je.Tensor)&&(l("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),_=v();let s=j1(t,this.config);if(!s||!s.tensor){l("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(v()-_),this.analyze("Get Image:");let i,a,h,y,d;this.config.async?(h=this.config.face.enabled?Bt(this,s.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",_=v(),h=this.config.face.enabled?await Bt(this,s.tensor):[],d=Math.trunc(v()-_),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?(f=this.models.posenet)==null?void 0:f.estimatePoses(s.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?T1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?M1(s.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",_=v(),this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?await((m=this.models.posenet)==null?void 0:m.estimatePoses(s.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?await T1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?await M1(s.tensor,this.config):[]),d=Math.trunc(v()-_),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(a=this.config.hand.enabled?(T=this.models.handpose)==null?void 0:T.estimateHands(s.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",_=v(),a=this.config.hand.enabled?await((E=this.models.handpose)==null?void 0:E.estimateHands(s.tensor,this.config)):[],d=Math.trunc(v()-_),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(y=this.config.object.enabled?R1(s.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",_=v(),y=this.config.object.enabled?await R1(s.tensor,this.config):[],d=Math.trunc(v()-_),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([h,i,a,y]=await Promise.all([h,i,a,y])),je.dispose(s.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(_=v(),p=[...N0(h),...W0(i),...k0(a),...O0(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(v()-_)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(v()-r),this.state="idle";let x={face:h,body:i,hand:a,gesture:p,object:y,performance:this.perf,canvas:s.canvas};A(x)})}async warmup(t={}){let n=v();if(t&&(this.config=ee(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let A=this.config.videoOptimized;this.config.videoOptimized=!1;let _;typeof createImageBitmap=="function"?_=await Ae(this,bt).call(this):typeof Image!="undefined"?_=await Ae(this,gt).call(this):_=await Ae(this,Tt).call(this),this.config.videoOptimized=A;let c=v();return this.config.debug&&l("Warmup",this.config.warmup,Math.round(c-n),"ms",_),_}};We=new WeakMap,He=new WeakMap,Ve=new WeakMap,Te=new WeakMap,ut=new WeakMap,Xe=new WeakMap,bt=new WeakMap,gt=new WeakMap,Tt=new WeakMap;0&&(module.exports={Human});
 //# sourceMappingURL=human.node-gpu.js.map
diff --git a/dist/human.node-gpu.js.map b/dist/human.node-gpu.js.map
index 16335364..a883ce68 100644
--- a/dist/human.node-gpu.js.map
+++ b/dist/human.node-gpu.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/tfjs/tf-node-gpu.ts", "../src/human.ts", "../src/helpers.ts", "../src/sysinfo.ts", "../src/tfjs/backend.ts", "../src/age/age.ts", "../src/profile.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/faceres/faceres.ts", "../src/faceall.ts", "../src/blazeface/facemesh.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/image.ts", "../src/image/imagefx.js", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts"],
-  "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore // typescript cannot infer type\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore // typescript cannot infer type\n        this.models.handpose,\n        // @ts-ignore // typescript cannot infer type\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    // @ts-ignore\n    if (typeof tf.node === 'undefined') {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      return null;\n    }\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    // @ts-ignore // tf.node is only defined when compiling for nodejs\n    const data = tf.node?.decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 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0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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+  "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        this.models.handpose,\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? <any>posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    let res;\n    if (typeof tf['node'] !== 'undefined') {\n      const data = tf['node'].decodeJpeg(img);\n      const expanded = data.expandDims(0);\n      this.tf.dispose(data);\n      // log('Input:', expanded);\n      res = await this.detect(expanded, this.config);\n      this.tf.dispose(expanded);\n    } else {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      /*\n      const input = await canvasJS.loadImage(img);\n      const canvas = canvasJS.createCanvas(input.width, input.height);\n      const ctx = canvas.getContext('2d');\n      ctx.drawImage(img, 0, 0, input.width, input.height);\n      res = await this.detect(input, this.config);\n      */\n    }\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input.toFloat(), angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 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0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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   "names": []
 }
diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js
index 844a9e03..e8b4db77 100644
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
@@ -5,13 +5,13 @@
   author: <https://github.com/vladmandic>'
   */
 
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dn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],fn=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],mn=[33,133,362,263,1,78,308],br=dn.map(e=>Oe[e]),gr=fn.map(e=>Oe[e]),Tr=mn.map(e=>Oe[e]);var Ct=ie.leftEyeLower0,Kt=ie.rightEyeLower0,ze={leftBounds:[Ct[0],Ct[Ct.length-1]],rightBounds:[Kt[0],Kt[Kt.length-1]]},_t={count:468,mouth:13,symmetryLine:[13,ie.midwayBetweenEyes[0]]},e0={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Ee={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function ot(e,t,n,A){for(let _=0;_<Jt.length;_++){let{key:c,indices:r}=Jt[_],o=ie[`${n}${c}`];if(!A||A.includes(c))for(let s=0;s<r.length;s++){let i=r[s];e[o[s]]=[t[i][0],t[i][1],(t[i][2]+e[o[s]][2])/2]}}}var Dt=class{constructor(t,n,A){var _,c;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=A,this.boxSize=((_=t==null?void 0:t.model)==null?void 0:_.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((c=t==null?void 0:t.model)==null?void 0:c.inputs[0].shape[2]),this.irisSize=(A==null?void 0:A.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,A,_){let c=Ne({startPoint:n.startPoint,endPoint:n.endPoint}),r=t.map(h=>[c[0]/this.meshSize*(h[0]-this.meshSize/2),c[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=A!==0?At(A,[0,0]):ct,s=A!==0?r.map(h=>[...$1(h,o),h[2]]):r,i=A!==0?Q1(_):ct,a=[...Pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(h=>[h[0]+ye(a,i[0]),h[1]+ye(a,i[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ze.leftBounds[0]][2],A=t[ze.rightBounds[0]][2];return n-A}getEyeBox(t,n,A,_,c=!1){let r=rt(nt(this.calculateLandmarksBoundingBox([t[A],t[_]]),this.irisEnlarge)),o=Ne(r),s=B.image.cropAndResize(n,[[r.startPoint[1]/this.meshSize,r.startPoint[0]/this.meshSize,r.endPoint[1]/this.meshSize,r.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return c&&B.ENV.flags.IS_BROWSER&&(s=B.image.flipLeftRight(s)),{box:r,boxSize:o,crop:s}}getEyeCoords(t,n,A,_=!1){let c=[];for(let r=0;r<Ee.numCoordinates;r++){let o=t[r*3],s=t[r*3+1],i=t[r*3+2];c.push([(_?1-o/this.irisSize:o/this.irisSize)*A[0]+n.startPoint[0],s/this.irisSize*A[1]+n.startPoint[1],i])}return{rawCoords:c,iris:c.slice(Ee.index)}}getAdjustedIrisCoords(t,n,A){let _=t[ie[`${A}EyeUpper0`][Ee.upperCenter]][2],c=t[ie[`${A}EyeLower0`][Ee.lowerCenter]][2],r=(_+c)/2;return n.map((o,s)=>{let i=r;return s===2?i=_:s===4&&(i=c),[o[0],o[1],i]})}async predict(t,n){let A=!1,_;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(_=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||_&&_.boxes&&(!n.face.mesh.enabled||_.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let r of _.boxes)this.storedBoxes.push({startPoint:r.box.startPoint.dataSync(),endPoint:r.box.endPoint.dataSync(),landmarks:r.landmarks,confidence:r.confidence});this.storedBoxes.length>0&&(A=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),A){if(!_||!_.boxes||_.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let r=0;r<this.storedBoxes.length;r++){let o=K1({startPoint:this.storedBoxes[r].startPoint,endPoint:this.storedBoxes[r].endPoint},_.scaleFactor),s=nt(o),i=rt(s),a=this.storedBoxes[r].landmarks.arraySync(),h=this.storedBoxes[r].confidence;this.storedBoxes[r]={...i,confidence:h,landmarks:a}}}_&&_.boxes&&_.boxes.forEach(r=>{r.box.startPoint.dispose(),r.box.endPoint.dispose(),r.landmarks.dispose()});let c=B.tidy(()=>this.storedBoxes.map((r,o)=>{let s=r.confidence,i,a=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=_t.count?_t.symmetryLine:e0.symmetryLine;a=Yt(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=At(-a,b),n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{h=ct;let u=t.clone();n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:r,faceConfidence:null,boxConfidence:s,confidence:r.confidence,image:i};let[,y,d]=this.meshDetector.predict(i),p=y.dataSync()[0];if(p<n.face.detector.minConfidence)return null;let f=B.reshape(d,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:u,boxSize:w,crop:b}=this.getEyeBox(f,i,ze.leftBounds[0],ze.leftBounds[1],!0),{box:M,boxSize:R,crop:H}=this.getEyeBox(f,i,ze.rightBounds[0],ze.rightBounds[1]),D=this.irisModel.predict(B.concat([b,H])).dataSync(),Mt=D.slice(0,Ee.numCoordinates*3),{rawCoords:Fe,iris:me}=this.getEyeCoords(Mt,u,w,!0),U0=D.slice(Ee.numCoordinates*3),{rawCoords:k1,iris:Y0}=this.getEyeCoords(U0,M,R),I1=this.getLeftToRightEyeDepthDifference(f);Math.abs(I1)<30?(ot(f,Fe,"left",null),ot(f,k1,"right",null)):I1<1?ot(f,Fe,"left",["EyeUpper0","EyeLower0"]):ot(f,k1,"right",["EyeUpper0","EyeLower0"]);let J0=this.getAdjustedIrisCoords(f,me,"left"),C0=this.getAdjustedIrisCoords(f,Y0,"right");f=f.concat(J0).concat(C0)}let m=this.transformRawCoords(f,r,a,h);r=nt(this.calculateLandmarksBoundingBox(m),1.5);let T=B.tensor2d(m);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=_t.count?_t.symmetryLine:e0.symmetryLine;a=Yt(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=At(-a,b),i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let v={coords:T,box:r,faceConfidence:p,boxConfidence:s,image:i,rawCoords:f},k=rt(r);return this.storedBoxes[o]={...k,landmarks:m,confidence:r.confidence,faceConfidence:p},v}));return c=c.filter(r=>r!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(r=>r.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(t){let n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c,landmarks:t}}};var $t=class{constructor(t,n,A,_){this.facePipeline=new Dt(t,n,A),this.config=_}async estimateFaces(t,n){let A=await this.facePipeline.predict(t,n),_=[];for(let c of A||[]){if(c.isDisposedInternal)continue;let r=c.coords?c.coords.arraySync():[],o=r.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),s={};if(r&&r.length>0)for(let h of Object.keys(ie))s[h]=ie[h].map(y=>r[y]);let i=c.box?[Math.max(0,c.box.startPoint[0]),Math.max(0,c.box.startPoint[1]),Math.min(t.shape[2],c.box.endPoint[0])-Math.max(0,c.box.startPoint[0]),Math.min(t.shape[1],c.box.endPoint[1])-Math.max(0,c.box.startPoint[1])]:0,a=c.box?[c.box.startPoint[0]/t.shape[2],c.box.startPoint[1]/t.shape[1],(c.box.endPoint[0]-c.box.startPoint[0])/t.shape[2],(c.box.endPoint[1]-c.box.startPoint[1])/t.shape[1]]:[];_.push({confidence:Math.round(100*c.faceConfidence||100*c.boxConfidence||0)/100,boxConfidence:Math.round(100*c.boxConfidence)/100,faceConfidence:Math.round(100*c.faceConfidence)/100,box:i,boxRaw:a,mesh:r,meshRaw:o,annotations:s,image:c.image?c.image.clone():null}),c.coords&&c.coords.dispose(),c.image&&c.image.dispose()}return _}},X=[null,null,null];async function e1(e){return!X[0]&&e.face.enabled||!X[1]&&e.face.mesh.enabled||!X[2]&&e.face.iris.enabled?(X=await Promise.all([!X[0]&&e.face.enabled?J1(e):null,!X[1]&&e.face.mesh.enabled?Qt.loadGraphModel(W(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!X[2]&&e.face.iris.enabled?Qt.loadGraphModel(W(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!X[1]||!X[1].modelUrl?l("load model failed:",e.face.mesh.modelPath):e.debug&&l("load model:",X[1].modelUrl)),e.face.iris.enabled&&(!X[2]||!X[1].modelUrl?l("load model failed:",e.face.iris.modelPath):e.debug&&l("load model:",X[2].modelUrl))):e.debug&&(l("cached model:",X[0].model.modelUrl),l("cached model:",X[1].modelUrl),l("cached model:",X[2].modelUrl)),new $t(X[0],X[1],X[2],e)}var t0=ue,n0=Oe;var i1={};ce(i1,{PoseNet:()=>a1,load:()=>x1});var u0=L(I());var r0=L(I());function pn(e){let[t,n,A,_]=e;return{offsets:t,heatmap:n,displacementFwd:A,displacementBwd:_}}var t1=class{constructor(t){this.model=t}predict(t){return r0.tidy(()=>{let A=t.toFloat().div(127.5).sub(1).expandDims(0),c=this.model.predict(A).map(o=>o.squeeze([0])),r=pn(c);return{heatmapScores:r.heatmap.sigmoid(),offsets:r.offsets,displacementFwd:r.displacementFwd,displacementBwd:r.displacementBwd}})}dispose(){this.model.dispose()}};function n1(e){return Math.floor(e/2)}var r1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(n1(t),t);)this.exchange(t,n1(t)),t=n1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=A}};function un(e,t,n,A,_,c){let[r,o]=c.shape,s=!0,i=Math.max(n-_,0),a=Math.min(n+_+1,r);for(let h=i;h<a;++h){let y=Math.max(A-_,0),d=Math.min(A+_+1,o);for(let p=y;p<d;++p)if(c.get(h,p,e)>t){s=!1;break}if(!s)break}return s}function c0(e,t,n){let[A,_,c]=n.shape,r=new r1(A*_*c,({score:o})=>o);for(let o=0;o<A;++o)for(let s=0;s<_;++s)for(let i=0;i<c;++i){let a=n.get(o,s,i);a<e||un(i,a,o,s,t,n)&&r.enqueue({score:a,part:{heatmapY:o,heatmapX:s,id:i}})}return r}var ve=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Re=ve.length,ke=ve.reduce((e,t,n)=>(e[t]=n,e),{}),bn=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Lr=bn.map(([e,t])=>[ke[e],ke[t]]),A0=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function c1(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function st(e,t,n){let{heatmapY:A,heatmapX:_,id:c}=e,{y:r,x:o}=c1(A,_,c,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+r}}function A1(e,t,n){return e<t?t:e>n?n:e}function _0(e,t,n,A){let _=n-e,c=A-t;return _*_+c*c}function _1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var ne=L(I());function o0(e,t){let n=t.shape[0],A=new Float32Array(n);for(let _=0;_<n;_++){let c=t.get(_,0),r=t.get(_,1);A[_]=e.get(c,r,_)}return A}function gn(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function Tn(e,t){let n=[];for(let A=0;A<Re;A++){let _=e.get(A,0).valueOf(),c=e.get(A,1).valueOf(),{x:r,y:o}=gn(_,c,A,t);n.push(o),n.push(r)}return ne.tensor2d(n,[Re,2])}function s0(e,t,n){return ne.tidy(()=>e.toTensor().mul(ne.scalar(t,"int32")).toFloat().add(Tn(e,n)))}function Pn(e,t){return ne.tidy(()=>{let n=e.div(ne.scalar(t,"int32"));return e.sub(n.mul(ne.scalar(t,"int32")))})}function h0(e){let[t,n,A]=e.shape;return ne.tidy(()=>{let c=e.reshape([t*n,A]).argMax(0),r=c.div(ne.scalar(n,"int32")).expandDims(1),o=Pn(c,n).expandDims(1);return ne.concat([r,o],1)})}var i0=A0.map(([e,t])=>[ke[e],ke[t]]),o1=i0.map(([,e])=>e),a0=i0.map(([e])=>e),Mn=16;function zn(e,t,n){let A=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,A+e)}}function s1(e,t,n,A){return{y:A1(Math.round(e.y/t),0,n-1),x:A1(Math.round(e.x/t),0,A-1)}}function x0(e,t,n,A,_,c,r,o=2){let[s,i]=A.shape,a=s1(t.position,c,s,i),h=zn(e,a,r),d=_1(t.position,h);for(let f=0;f<o;f++){let m=s1(d,c,s,i),T=c1(m.y,m.x,n,_);d=_1({x:m.x*c,y:m.y*c},{x:T.x,y:T.y})}let p=s1(d,c,s,i),x=A.get(p.y,p.x,n);return{position:d,part:ve[n],score:x}}function y0(e,t,n,A,_,c){let r=t.shape[2],o=o1.length,s=new Array(r),{part:i,score:a}=e,h=st(i,A,n);s[i.id]={score:a,part:ve[i.id],position:h};for(let y=o-1;y>=0;--y){let d=o1[y],p=a0[y];s[d]&&!s[p]&&(s[p]=x0(y,s[d],p,t,n,A,c))}for(let y=0;y<o;++y){let d=a0[y],p=o1[y];s[d]&&!s[p]&&(s[p]=x0(y,s[d],p,t,n,A,_))}return s}async function w0(e,t,n){let A=0,_=h0(e),c=await Promise.all([e.buffer(),t.buffer(),_.buffer()]),r=c[0],o=c[1],s=c[2],i=s0(s,Mn,o),a=await i.buffer(),y=Array.from(o0(r,s)).map((p,x)=>(A+=p,{position:{y:a.get(x,0),x:a.get(x,1)},part:ve[x],score:p})),d=y.filter(p=>p.score>n);return _.dispose(),i.dispose(),{keypoints:d,score:A/y.length}}var En=1,l0=16;function d0(e,t,{x:n,y:A},_){return e.some(({keypoints:c})=>{let r=c[_].position;return _0(A,n,r.y,r.x)<=t})}function vn(e,t,n){return n.reduce((_,{position:c,score:r},o)=>(d0(e,t,c,o)||(_+=r),_),0)/n.length}function f0(e,t,n,A,_,c,r){let o=[],s=c0(r,En,e),i=_^2;for(;o.length<c&&!s.empty();){let a=s.dequeue(),h=st(a.part,l0,t);if(d0(o,i,h,a.part.id))continue;let y=y0(a,e,t,l0,n,A),d=vn(o,i,y);d>r&&o.push({keypoints:y,score:Math.round(100*d)/100})}return o}async function m0(e){return Promise.all(e.map(t=>t.buffer()))}function Rn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:A,part:_,position:c})=>({score:A,part:_,position:{x:Math.trunc(c.x*n),y:Math.trunc(c.y*t)}}))}}function p0(e,[t,n]){let A=e.squeeze(0),_=A.resizeBilinear([t,n]);return A.dispose(),_}function h1(e,[t,n],[A,_]){return e.map(r=>Rn(r,t/A,n/_))}var be;async function Sn(e,t,n,A){return new Promise(async _=>{let c=await m0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),r=c[0],o=c[1],s=c[2],i=c[3],a=await f0(r,o,s,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=h1(a,[e.shape[1],e.shape[2]],[A,A]);_(h)})}async function jn(e,t,n,A){return new Promise(async _=>{let c=await w0(t.heatmapScores,t.offsets,n.body.scoreThreshold),r=h1([c],[e.shape[1],e.shape[2]],[A,A]);_(r)})}var a1=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let A=p0(t,[this.inputSize,this.inputSize]),_=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await jn(t,_,n,this.inputSize):await Sn(t,_,n,this.inputSize);return _.heatmapScores.dispose(),_.offsets.dispose(),_.displacementFwd.dispose(),_.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function x1(e){be?e.debug&&l("cached model:",be.modelUrl):(be=await u0.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),!be||!be.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",be.modelUrl));let t=new t1(be);return new a1(t)}var f1={};ce(f1,{HandPose:()=>u1,load:()=>b1});var m1=L(I());var P=L(I());var b0=L(I());function ht(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ie(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function g0(e,t,n){let 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n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c}}};var 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j0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],W0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var J;async function T1(e){return J?e.debug&&l("cached model:",J.modelUrl):(J=await le.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),J.width=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[2].size),J.height=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[1].size),!J||!J.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",J.modelUrl)),J}async function P1(e,t){if(!J||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},A=le.image.resizeBilinear(e,[J.width,J.height],!1),_=le.div(A,[255]);A.dispose();let c;if(t.profile){let a=await le.profile(()=>J.predict(_));c=a.result.find(h=>h.size===195||h.size===155).dataSync(),a.result.forEach(h=>h.dispose()),Y("blazepose",a)}else{let a=await J.predict(_);c=a.find(h=>h.size===195||h.size===155).dataSync(),a.forEach(h=>h.dispose())}_.dispose();let r=[],o=c.length===195?j0:W0,s=5;for(let 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dn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],fn=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],mn=[33,133,362,263,1,78,308],br=dn.map(e=>Oe[e]),gr=fn.map(e=>Oe[e]),Tr=mn.map(e=>Oe[e]);var Ct=ie.leftEyeLower0,Kt=ie.rightEyeLower0,ze={leftBounds:[Ct[0],Ct[Ct.length-1]],rightBounds:[Kt[0],Kt[Kt.length-1]]},_t={count:468,mouth:13,symmetryLine:[13,ie.midwayBetweenEyes[0]]},e0={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ve={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function ot(e,t,n,A){for(let _=0;_<Jt.length;_++){let{key:c,indices:r}=Jt[_],o=ie[`${n}${c}`];if(!A||A.includes(c))for(let s=0;s<r.length;s++){let i=r[s];e[o[s]]=[t[i][0],t[i][1],(t[i][2]+e[o[s]][2])/2]}}}var Dt=class{constructor(t,n,A){var _,c;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=A,this.boxSize=((_=t==null?void 0:t.model)==null?void 0:_.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((c=t==null?void 0:t.model)==null?void 0:c.inputs[0].shape[2]),this.irisSize=(A==null?void 0:A.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,A,_){let c=Ne({startPoint:n.startPoint,endPoint:n.endPoint}),r=t.map(h=>[c[0]/this.meshSize*(h[0]-this.meshSize/2),c[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=A!==0?At(A,[0,0]):ct,s=A!==0?r.map(h=>[...$1(h,o),h[2]]):r,i=A!==0?Q1(_):ct,a=[...Pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(h=>[h[0]+ye(a,i[0]),h[1]+ye(a,i[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ze.leftBounds[0]][2],A=t[ze.rightBounds[0]][2];return n-A}getEyeBox(t,n,A,_,c=!1){let r=rt(nt(this.calculateLandmarksBoundingBox([t[A],t[_]]),this.irisEnlarge)),o=Ne(r),s=B.image.cropAndResize(n,[[r.startPoint[1]/this.meshSize,r.startPoint[0]/this.meshSize,r.endPoint[1]/this.meshSize,r.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return c&&B.ENV.flags.IS_BROWSER&&(s=B.image.flipLeftRight(s)),{box:r,boxSize:o,crop:s}}getEyeCoords(t,n,A,_=!1){let c=[];for(let r=0;r<ve.numCoordinates;r++){let o=t[r*3],s=t[r*3+1],i=t[r*3+2];c.push([(_?1-o/this.irisSize:o/this.irisSize)*A[0]+n.startPoint[0],s/this.irisSize*A[1]+n.startPoint[1],i])}return{rawCoords:c,iris:c.slice(ve.index)}}getAdjustedIrisCoords(t,n,A){let _=t[ie[`${A}EyeUpper0`][ve.upperCenter]][2],c=t[ie[`${A}EyeLower0`][ve.lowerCenter]][2],r=(_+c)/2;return n.map((o,s)=>{let i=r;return s===2?i=_:s===4&&(i=c),[o[0],o[1],i]})}async predict(t,n){let A=!1,_;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(_=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||_&&_.boxes&&(!n.face.mesh.enabled||_.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let r of _.boxes)this.storedBoxes.push({startPoint:r.box.startPoint.dataSync(),endPoint:r.box.endPoint.dataSync(),landmarks:r.landmarks,confidence:r.confidence});this.storedBoxes.length>0&&(A=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),A){if(!_||!_.boxes||_.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let r=0;r<this.storedBoxes.length;r++){let o=K1({startPoint:this.storedBoxes[r].startPoint,endPoint:this.storedBoxes[r].endPoint},_.scaleFactor),s=nt(o),i=rt(s),a=this.storedBoxes[r].landmarks.arraySync(),h=this.storedBoxes[r].confidence;this.storedBoxes[r]={...i,confidence:h,landmarks:a}}}_&&_.boxes&&_.boxes.forEach(r=>{r.box.startPoint.dispose(),r.box.endPoint.dispose(),r.landmarks.dispose()});let c=B.tidy(()=>this.storedBoxes.map((r,o)=>{let s=r.confidence,i,a=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=_t.count?_t.symmetryLine:e0.symmetryLine;a=Yt(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=At(-a,b),n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{h=ct;let u=t.clone();n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:r,faceConfidence:null,boxConfidence:s,confidence:r.confidence,image:i};let[,y,d]=this.meshDetector.predict(i),p=y.dataSync()[0];if(p<n.face.detector.minConfidence)return null;let f=B.reshape(d,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:u,boxSize:w,crop:b}=this.getEyeBox(f,i,ze.leftBounds[0],ze.leftBounds[1],!0),{box:M,boxSize:R,crop:H}=this.getEyeBox(f,i,ze.rightBounds[0],ze.rightBounds[1]),D=this.irisModel.predict(B.concat([b,H])).dataSync(),Mt=D.slice(0,ve.numCoordinates*3),{rawCoords:Fe,iris:me}=this.getEyeCoords(Mt,u,w,!0),U0=D.slice(ve.numCoordinates*3),{rawCoords:k1,iris:Y0}=this.getEyeCoords(U0,M,R),I1=this.getLeftToRightEyeDepthDifference(f);Math.abs(I1)<30?(ot(f,Fe,"left",null),ot(f,k1,"right",null)):I1<1?ot(f,Fe,"left",["EyeUpper0","EyeLower0"]):ot(f,k1,"right",["EyeUpper0","EyeLower0"]);let J0=this.getAdjustedIrisCoords(f,me,"left"),C0=this.getAdjustedIrisCoords(f,Y0,"right");f=f.concat(J0).concat(C0)}let m=this.transformRawCoords(f,r,a,h);r=nt(this.calculateLandmarksBoundingBox(m),1.5);let T=B.tensor2d(m);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=_t.count?_t.symmetryLine:e0.symmetryLine;a=Yt(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t.toFloat(),a,0,M);h=At(-a,b),i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let E={coords:T,box:r,faceConfidence:p,boxConfidence:s,image:i,rawCoords:f},k=rt(r);return this.storedBoxes[o]={...k,landmarks:m,confidence:r.confidence,faceConfidence:p},E}));return c=c.filter(r=>r!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(r=>r.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(t){let n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c,landmarks:t}}};var $t=class{constructor(t,n,A,_){this.facePipeline=new Dt(t,n,A),this.config=_}async estimateFaces(t,n){let A=await this.facePipeline.predict(t,n),_=[];for(let c of A||[]){if(c.isDisposedInternal)continue;let r=c.coords?c.coords.arraySync():[],o=r.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),s={};if(r&&r.length>0)for(let h of Object.keys(ie))s[h]=ie[h].map(y=>r[y]);let i=c.box?[Math.max(0,c.box.startPoint[0]),Math.max(0,c.box.startPoint[1]),Math.min(t.shape[2],c.box.endPoint[0])-Math.max(0,c.box.startPoint[0]),Math.min(t.shape[1],c.box.endPoint[1])-Math.max(0,c.box.startPoint[1])]:0,a=c.box?[c.box.startPoint[0]/t.shape[2],c.box.startPoint[1]/t.shape[1],(c.box.endPoint[0]-c.box.startPoint[0])/t.shape[2],(c.box.endPoint[1]-c.box.startPoint[1])/t.shape[1]]:[];_.push({confidence:Math.round(100*c.faceConfidence||100*c.boxConfidence||0)/100,boxConfidence:Math.round(100*c.boxConfidence)/100,faceConfidence:Math.round(100*c.faceConfidence)/100,box:i,boxRaw:a,mesh:r,meshRaw:o,annotations:s,image:c.image?c.image.clone():null}),c.coords&&c.coords.dispose(),c.image&&c.image.dispose()}return _}},X=[null,null,null];async function e1(e){return!X[0]&&e.face.enabled||!X[1]&&e.face.mesh.enabled||!X[2]&&e.face.iris.enabled?(X=await Promise.all([!X[0]&&e.face.enabled?J1(e):null,!X[1]&&e.face.mesh.enabled?Qt.loadGraphModel(W(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!X[2]&&e.face.iris.enabled?Qt.loadGraphModel(W(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!X[1]||!X[1].modelUrl?l("load model failed:",e.face.mesh.modelPath):e.debug&&l("load model:",X[1].modelUrl)),e.face.iris.enabled&&(!X[2]||!X[1].modelUrl?l("load model failed:",e.face.iris.modelPath):e.debug&&l("load model:",X[2].modelUrl))):e.debug&&(l("cached model:",X[0].model.modelUrl),l("cached model:",X[1].modelUrl),l("cached model:",X[2].modelUrl)),new $t(X[0],X[1],X[2],e)}var t0=ue,n0=Oe;var i1={};ce(i1,{PoseNet:()=>a1,load:()=>x1});var u0=L(I());var r0=L(I());function pn(e){let[t,n,A,_]=e;return{offsets:t,heatmap:n,displacementFwd:A,displacementBwd:_}}var t1=class{constructor(t){this.model=t}predict(t){return r0.tidy(()=>{let A=t.toFloat().div(127.5).sub(1).expandDims(0),c=this.model.predict(A).map(o=>o.squeeze([0])),r=pn(c);return{heatmapScores:r.heatmap.sigmoid(),offsets:r.offsets,displacementFwd:r.displacementFwd,displacementBwd:r.displacementBwd}})}dispose(){this.model.dispose()}};function n1(e){return Math.floor(e/2)}var r1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(n1(t),t);)this.exchange(t,n1(t)),t=n1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=A}};function un(e,t,n,A,_,c){let[r,o]=c.shape,s=!0,i=Math.max(n-_,0),a=Math.min(n+_+1,r);for(let h=i;h<a;++h){let y=Math.max(A-_,0),d=Math.min(A+_+1,o);for(let p=y;p<d;++p)if(c.get(h,p,e)>t){s=!1;break}if(!s)break}return s}function c0(e,t,n){let[A,_,c]=n.shape,r=new r1(A*_*c,({score:o})=>o);for(let o=0;o<A;++o)for(let s=0;s<_;++s)for(let i=0;i<c;++i){let a=n.get(o,s,i);a<e||un(i,a,o,s,t,n)&&r.enqueue({score:a,part:{heatmapY:o,heatmapX:s,id:i}})}return r}var Ee=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Re=Ee.length,ke=Ee.reduce((e,t,n)=>(e[t]=n,e),{}),bn=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Lr=bn.map(([e,t])=>[ke[e],ke[t]]),A0=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function c1(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function st(e,t,n){let{heatmapY:A,heatmapX:_,id:c}=e,{y:r,x:o}=c1(A,_,c,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+r}}function A1(e,t,n){return e<t?t:e>n?n:e}function _0(e,t,n,A){let _=n-e,c=A-t;return _*_+c*c}function _1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var ne=L(I());function o0(e,t){let n=t.shape[0],A=new Float32Array(n);for(let _=0;_<n;_++){let c=t.get(_,0),r=t.get(_,1);A[_]=e.get(c,r,_)}return A}function gn(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function Tn(e,t){let n=[];for(let A=0;A<Re;A++){let _=e.get(A,0).valueOf(),c=e.get(A,1).valueOf(),{x:r,y:o}=gn(_,c,A,t);n.push(o),n.push(r)}return ne.tensor2d(n,[Re,2])}function s0(e,t,n){return ne.tidy(()=>e.toTensor().mul(ne.scalar(t,"int32")).toFloat().add(Tn(e,n)))}function Pn(e,t){return ne.tidy(()=>{let n=e.div(ne.scalar(t,"int32"));return e.sub(n.mul(ne.scalar(t,"int32")))})}function h0(e){let[t,n,A]=e.shape;return ne.tidy(()=>{let c=e.reshape([t*n,A]).argMax(0),r=c.div(ne.scalar(n,"int32")).expandDims(1),o=Pn(c,n).expandDims(1);return ne.concat([r,o],1)})}var i0=A0.map(([e,t])=>[ke[e],ke[t]]),o1=i0.map(([,e])=>e),a0=i0.map(([e])=>e),Mn=16;function zn(e,t,n){let A=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,A+e)}}function s1(e,t,n,A){return{y:A1(Math.round(e.y/t),0,n-1),x:A1(Math.round(e.x/t),0,A-1)}}function x0(e,t,n,A,_,c,r,o=2){let[s,i]=A.shape,a=s1(t.position,c,s,i),h=zn(e,a,r),d=_1(t.position,h);for(let f=0;f<o;f++){let m=s1(d,c,s,i),T=c1(m.y,m.x,n,_);d=_1({x:m.x*c,y:m.y*c},{x:T.x,y:T.y})}let p=s1(d,c,s,i),x=A.get(p.y,p.x,n);return{position:d,part:Ee[n],score:x}}function y0(e,t,n,A,_,c){let r=t.shape[2],o=o1.length,s=new Array(r),{part:i,score:a}=e,h=st(i,A,n);s[i.id]={score:a,part:Ee[i.id],position:h};for(let y=o-1;y>=0;--y){let d=o1[y],p=a0[y];s[d]&&!s[p]&&(s[p]=x0(y,s[d],p,t,n,A,c))}for(let y=0;y<o;++y){let d=a0[y],p=o1[y];s[d]&&!s[p]&&(s[p]=x0(y,s[d],p,t,n,A,_))}return s}async function w0(e,t,n){let A=0,_=h0(e),c=await Promise.all([e.buffer(),t.buffer(),_.buffer()]),r=c[0],o=c[1],s=c[2],i=s0(s,Mn,o),a=await i.buffer(),y=Array.from(o0(r,s)).map((p,x)=>(A+=p,{position:{y:a.get(x,0),x:a.get(x,1)},part:Ee[x],score:p})),d=y.filter(p=>p.score>n);return _.dispose(),i.dispose(),{keypoints:d,score:A/y.length}}var vn=1,l0=16;function d0(e,t,{x:n,y:A},_){return e.some(({keypoints:c})=>{let r=c[_].position;return _0(A,n,r.y,r.x)<=t})}function En(e,t,n){return n.reduce((_,{position:c,score:r},o)=>(d0(e,t,c,o)||(_+=r),_),0)/n.length}function f0(e,t,n,A,_,c,r){let o=[],s=c0(r,vn,e),i=_^2;for(;o.length<c&&!s.empty();){let a=s.dequeue(),h=st(a.part,l0,t);if(d0(o,i,h,a.part.id))continue;let y=y0(a,e,t,l0,n,A),d=En(o,i,y);d>r&&o.push({keypoints:y,score:Math.round(100*d)/100})}return o}async function m0(e){return Promise.all(e.map(t=>t.buffer()))}function Rn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:A,part:_,position:c})=>({score:A,part:_,position:{x:Math.trunc(c.x*n),y:Math.trunc(c.y*t)}}))}}function p0(e,[t,n]){let A=e.squeeze(0),_=A.resizeBilinear([t,n]);return A.dispose(),_}function h1(e,[t,n],[A,_]){return e.map(r=>Rn(r,t/A,n/_))}var be;async function Sn(e,t,n,A){return new Promise(async _=>{let c=await m0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),r=c[0],o=c[1],s=c[2],i=c[3],a=await f0(r,o,s,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=h1(a,[e.shape[1],e.shape[2]],[A,A]);_(h)})}async function jn(e,t,n,A){return new Promise(async _=>{let c=await w0(t.heatmapScores,t.offsets,n.body.scoreThreshold),r=h1([c],[e.shape[1],e.shape[2]],[A,A]);_(r)})}var a1=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let A=p0(t,[this.inputSize,this.inputSize]),_=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await jn(t,_,n,this.inputSize):await Sn(t,_,n,this.inputSize);return _.heatmapScores.dispose(),_.offsets.dispose(),_.displacementFwd.dispose(),_.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function x1(e){be?e.debug&&l("cached model:",be.modelUrl):(be=await u0.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),!be||!be.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",be.modelUrl));let t=new t1(be);return new a1(t)}var f1={};ce(f1,{HandPose:()=>u1,load:()=>b1});var m1=L(I());var P=L(I());var b0=L(I());function ht(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ie(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function g0(e,t,n){let 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n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c}}};var 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j0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],W0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var J;async function T1(e){return J?e.debug&&l("cached model:",J.modelUrl):(J=await le.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),J.width=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[2].size),J.height=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[1].size),!J||!J.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",J.modelUrl)),J}async function P1(e,t){if(!J||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},A=le.image.resizeBilinear(e,[J.width,J.height],!1),_=le.div(A,[255]);A.dispose();let c;if(t.profile){let a=await le.profile(()=>J.predict(_));c=a.result.find(h=>h.size===195||h.size===155).dataSync(),a.result.forEach(h=>h.dispose()),Y("blazepose",a)}else{let a=await J.predict(_);c=a.find(h=>h.size===195||h.size===155).dataSync(),a.forEach(h=>h.dispose())}_.dispose();let r=[],o=c.length===195?j0:W0,s=5;for(let 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cannot set backend:",this.config.backend,_)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(l("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let _=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&l(`gl version:${_.getParameter(_.VERSION)} renderer:${_.getParameter(_.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(E()-A)}});gt.set(this,async()=>{let t=(_,c="application/octet-stream")=>fetch(`data:${c};base64,${_}`).then(r=>r.blob()),n,A;switch(this.config.warmup){case"face":n=await t(pt);break;case"full":n=await t(ut);break;default:n=null}if(n){let _=await createImageBitmap(n);A=await this.detect(_,this.config),_.close()}return A});Tt.set(this,async()=>new Promise(t=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+pt;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+ut;break;default:n=null}let _=new Image;_.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=_.naturalWidth,c.height=_.naturalHeight;let r=c.getContext("2d");r==null||r.drawImage(_,0,0);let o=await this.detect(c,this.config);t(o)},n?_.src=n:t(null)}));Pt.set(this,async()=>{var r;if(typeof je.node=="undefined")return this.config.debug&&l("Warmup tfjs-node not loaded"),null;let t=o=>Buffer.from(o,"base64"),n;if(this.config.warmup==="face"&&(n=t(pt)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(ut)),!n)return null;let A=(r=je.node)==null?void 0:r.decodeJpeg(n),_=A.expandDims(0);this.tf.dispose(A);let c=await this.detect(_,this.config);return this.tf.dispose(_),c});this.tf=je,this.draw=N1,this.version=q0,this.config=ee(j,t),this.state="idle",pe(this,We,0),pe(this,He,!1),pe(this,Ve,!1),pe(this,Te,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>W1(n,this.config),this.classes={facemesh:Gt,age:vt,gender:St,emotion:kt,faceres:Xt,body:this.config.body.modelPath.includes("posenet")?i1:g1,hand:f1,nanodet:E1},this.faceTriangulation=t0,this.faceUVMap=n0,this.sysinfo=H1()}profileData(){return this.config.profile?Ue:{}}similarity(t,n){return this.config.face.description.enabled?qt(t,n):this.config.face.embedding.enabled?q1(t,n):0}enhance(t){return Bt(t)}match(t,n,A=0){return B1(t,n,A)}async load(t={}){this.state="load";let n=E();t&&(this.config=ee(this.config,t)),Ae(this,Te)&&(this.config.debug&&l(`version: ${this.version}`),this.config.debug&&l(`tfjs version: ${this.tf.version_core}`),this.config.debug&&l("platform:",this.sysinfo.platform),this.config.debug&&l("agent:",this.sysinfo.agent),await Ae(this,Xe).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&l("configuration:",this.config),this.config.debug&&l("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?e1(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Rt(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Ot(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Zt(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Ht(this.config):null),this.models.handpose||(this.config.hand.enabled?b1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?x1(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?T1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?M1(this.config):null),this.models.nanodet||(this.config.object.enabled?R1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Ft(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await e1(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Rt(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Ot(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Zt(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Ht(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await b1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await x1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await T1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await M1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await R1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Ft(this.config))),Ae(this,Te)&&(this.config.debug&&l("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),pe(this,Te,!1));let A=Math.trunc(E()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async detect(t,n={}){return new Promise(async A=>{var f,m,T,v;this.state="config";let _;this.config=ee(this.config,n),this.state="check";let c=Ae(this,bt).call(this,t);c&&(l(c,t),A({error:c}));let r=E();await Ae(this,Xe).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&j1 instanceof ImageBitmap||t instanceof je.Tensor)&&(l("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),_=E();let s=W1(t,this.config);if(!s||!s.tensor){l("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(E()-_),this.analyze("Get Image:");let i,a,h,y,d;this.config.async?(h=this.config.face.enabled?Ut(this,s.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",_=E(),h=this.config.face.enabled?await Ut(this,s.tensor):[],d=Math.trunc(E()-_),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?(f=this.models.posenet)==null?void 0:f.estimatePoses(s.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?P1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?z1(s.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",_=E(),this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?await((m=this.models.posenet)==null?void 0:m.estimatePoses(s.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?await P1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?await z1(s.tensor,this.config):[]),d=Math.trunc(E()-_),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(a=this.config.hand.enabled?(T=this.models.handpose)==null?void 0:T.estimateHands(s.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",_=E(),a=this.config.hand.enabled?await((v=this.models.handpose)==null?void 0:v.estimateHands(s.tensor,this.config)):[],d=Math.trunc(E()-_),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(y=this.config.object.enabled?S1(s.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",_=E(),y=this.config.object.enabled?await S1(s.tensor,this.config):[],d=Math.trunc(E()-_),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([h,i,a,y]=await Promise.all([h,i,a,y])),je.dispose(s.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(_=E(),p=[...O0(h),...N0(i),...I0(a),...k0(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(E()-_)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(E()-r),this.state="idle";let x={face:h,body:i,hand:a,gesture:p,object:y,performance:this.perf,canvas:s.canvas};A(x)})}async warmup(t={}){let n=E();if(t&&(this.config=ee(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let A=this.config.videoOptimized;this.config.videoOptimized=!1;let _;typeof createImageBitmap=="function"?_=await Ae(this,gt).call(this):typeof Image!="undefined"?_=await Ae(this,Tt).call(this):_=await Ae(this,Pt).call(this),this.config.videoOptimized=A;let c=E();return this.config.debug&&l("Warmup",this.config.warmup,Math.round(c-n),"ms",_),_}};We=new WeakMap,He=new WeakMap,Ve=new WeakMap,Te=new WeakMap,bt=new WeakMap,Xe=new WeakMap,gt=new WeakMap,Tt=new WeakMap,Pt=new WeakMap;0&&(module.exports={Human});
+2Q==`;var q0="1.5.1";var We,He,Ve,Te,bt,Xe,gt,Tt,Pt,B0=class{constructor(t={}){We.set(this,void 0);He.set(this,void 0);Ve.set(this,void 0);Te.set(this,void 0);this.analyze=(...t)=>{if(!Ae(this,He))return;let n=this.tf.engine().state.numTensors,A=Ae(this,We);pe(this,We,n);let _=n-A;_!==0&&l(...t,_)};bt.set(this,t=>{if(!Ae(this,Ve))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof je.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Xe.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let A=v();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&l("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&l("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let _=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),c=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&l(`wasm execution: ${_?"SIMD":"no SIMD"} ${c?"multithreaded":"singlethreaded"}`),this.config.debug&&!_&&l("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&F1();try{await this.tf.setBackend(this.config.backend)}catch(_){l("error: cannot set backend:",this.config.backend,_)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(l("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let _=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&l(`gl version:${_.getParameter(_.VERSION)} renderer:${_.getParameter(_.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(v()-A)}});gt.set(this,async()=>{let t=(_,c="application/octet-stream")=>fetch(`data:${c};base64,${_}`).then(r=>r.blob()),n,A;switch(this.config.warmup){case"face":n=await t(pt);break;case"full":n=await t(ut);break;default:n=null}if(n){let _=await createImageBitmap(n);A=await this.detect(_,this.config),_.close()}return A});Tt.set(this,async()=>new Promise(t=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+pt;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+ut;break;default:n=null}let _=new Image;_.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=_.naturalWidth,c.height=_.naturalHeight;let r=c.getContext("2d");r==null||r.drawImage(_,0,0);let o=await this.detect(c,this.config);t(o)},n?_.src=n:t(null)}));Pt.set(this,async()=>{let t=_=>Buffer.from(_,"base64"),n;if(this.config.warmup==="face"&&(n=t(pt)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(ut)),!n)return null;let A;if(typeof je.node!="undefined"){let _=je.node.decodeJpeg(n),c=_.expandDims(0);this.tf.dispose(_),A=await this.detect(c,this.config),this.tf.dispose(c)}else this.config.debug&&l("Warmup tfjs-node not loaded");return A});this.tf=je,this.draw=N1,this.version=q0,this.config=ee(j,t),this.state="idle",pe(this,We,0),pe(this,He,!1),pe(this,Ve,!1),pe(this,Te,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>W1(n,this.config),this.classes={facemesh:Gt,age:Et,gender:St,emotion:kt,faceres:Xt,body:this.config.body.modelPath.includes("posenet")?i1:g1,hand:f1,nanodet:v1},this.faceTriangulation=t0,this.faceUVMap=n0,this.sysinfo=H1()}profileData(){return this.config.profile?Ue:{}}similarity(t,n){return this.config.face.description.enabled?qt(t,n):this.config.face.embedding.enabled?q1(t,n):0}enhance(t){return Bt(t)}match(t,n,A=0){return B1(t,n,A)}async load(t={}){this.state="load";let n=v();t&&(this.config=ee(this.config,t)),Ae(this,Te)&&(this.config.debug&&l(`version: ${this.version}`),this.config.debug&&l(`tfjs version: ${this.tf.version_core}`),this.config.debug&&l("platform:",this.sysinfo.platform),this.config.debug&&l("agent:",this.sysinfo.agent),await Ae(this,Xe).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&l("configuration:",this.config),this.config.debug&&l("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?e1(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Rt(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Ot(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Zt(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Ht(this.config):null),this.models.handpose||(this.config.hand.enabled?b1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?x1(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?T1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?M1(this.config):null),this.models.nanodet||(this.config.object.enabled?R1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Ft(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await e1(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Rt(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Ot(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Zt(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Ht(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await b1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await x1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await T1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await M1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await R1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Ft(this.config))),Ae(this,Te)&&(this.config.debug&&l("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),pe(this,Te,!1));let A=Math.trunc(v()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async detect(t,n={}){return new Promise(async A=>{var f,m,T,E;this.state="config";let _;this.config=ee(this.config,n),this.state="check";let c=Ae(this,bt).call(this,t);c&&(l(c,t),A({error:c}));let r=v();await Ae(this,Xe).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&j1 instanceof ImageBitmap||t instanceof je.Tensor)&&(l("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),_=v();let s=W1(t,this.config);if(!s||!s.tensor){l("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(v()-_),this.analyze("Get Image:");let i,a,h,y,d;this.config.async?(h=this.config.face.enabled?Ut(this,s.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",_=v(),h=this.config.face.enabled?await Ut(this,s.tensor):[],d=Math.trunc(v()-_),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?(f=this.models.posenet)==null?void 0:f.estimatePoses(s.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?P1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?z1(s.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",_=v(),this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?await((m=this.models.posenet)==null?void 0:m.estimatePoses(s.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?await P1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?await z1(s.tensor,this.config):[]),d=Math.trunc(v()-_),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(a=this.config.hand.enabled?(T=this.models.handpose)==null?void 0:T.estimateHands(s.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",_=v(),a=this.config.hand.enabled?await((E=this.models.handpose)==null?void 0:E.estimateHands(s.tensor,this.config)):[],d=Math.trunc(v()-_),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(y=this.config.object.enabled?S1(s.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",_=v(),y=this.config.object.enabled?await S1(s.tensor,this.config):[],d=Math.trunc(v()-_),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([h,i,a,y]=await Promise.all([h,i,a,y])),je.dispose(s.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(_=v(),p=[...O0(h),...N0(i),...I0(a),...k0(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(v()-_)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(v()-r),this.state="idle";let x={face:h,body:i,hand:a,gesture:p,object:y,performance:this.perf,canvas:s.canvas};A(x)})}async warmup(t={}){let n=v();if(t&&(this.config=ee(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let A=this.config.videoOptimized;this.config.videoOptimized=!1;let _;typeof createImageBitmap=="function"?_=await Ae(this,gt).call(this):typeof Image!="undefined"?_=await Ae(this,Tt).call(this):_=await Ae(this,Pt).call(this),this.config.videoOptimized=A;let c=v();return this.config.debug&&l("Warmup",this.config.warmup,Math.round(c-n),"ms",_),_}};We=new WeakMap,He=new WeakMap,Ve=new WeakMap,Te=new WeakMap,bt=new WeakMap,Xe=new WeakMap,gt=new WeakMap,Tt=new WeakMap,Pt=new WeakMap;0&&(module.exports={Human});
 //# sourceMappingURL=human.node-wasm.js.map
diff --git a/dist/human.node-wasm.js.map b/dist/human.node-wasm.js.map
index d1529497..30ed0808 100644
--- a/dist/human.node-wasm.js.map
+++ b/dist/human.node-wasm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/tfjs/tf-node-wasm.ts", "../src/human.ts", "../src/helpers.ts", "../src/sysinfo.ts", "../src/tfjs/backend.ts", "../src/age/age.ts", "../src/profile.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/faceres/faceres.ts", "../src/faceall.ts", "../src/blazeface/facemesh.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/image.ts", "../src/image/imagefx.js", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts"],
-  "sourcesContent": ["export * from '@tensorflow/tfjs';\nexport * from '@tensorflow/tfjs-backend-wasm';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore // typescript cannot infer type\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore // typescript cannot infer type\n        this.models.handpose,\n        // @ts-ignore // typescript cannot infer type\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    // @ts-ignore\n    if (typeof tf.node === 'undefined') {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      return null;\n    }\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    // @ts-ignore // tf.node is only defined when compiling for nodejs\n    const data = tf.node?.decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 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0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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+  "sourcesContent": ["export * from '@tensorflow/tfjs';\nexport * from '@tensorflow/tfjs-backend-wasm';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        this.models.handpose,\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? <any>posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    let res;\n    if (typeof tf['node'] !== 'undefined') {\n      const data = tf['node'].decodeJpeg(img);\n      const expanded = data.expandDims(0);\n      this.tf.dispose(data);\n      // log('Input:', expanded);\n      res = await this.detect(expanded, this.config);\n      this.tf.dispose(expanded);\n    } else {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      /*\n      const input = await canvasJS.loadImage(img);\n      const canvas = canvasJS.createCanvas(input.width, input.height);\n      const ctx = canvas.getContext('2d');\n      ctx.drawImage(img, 0, 0, input.width, input.height);\n      res = await this.detect(input, this.config);\n      */\n    }\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input.toFloat(), angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 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0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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   "names": []
 }
diff --git a/dist/human.node.js b/dist/human.node.js
index 65e7fb6c..182228e9 100644
--- a/dist/human.node.js
+++ b/dist/human.node.js
@@ -5,13 +5,13 @@
   author: <https://github.com/vladmandic>'
   */
 
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dn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],fn=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],mn=[33,133,362,263,1,78,308],br=dn.map(e=>Oe[e]),gr=fn.map(e=>Oe[e]),Tr=mn.map(e=>Oe[e]);var Jt=ie.leftEyeLower0,Ct=ie.rightEyeLower0,ze={leftBounds:[Jt[0],Jt[Jt.length-1]],rightBounds:[Ct[0],Ct[Ct.length-1]]},At={count:468,mouth:13,symmetryLine:[13,ie.midwayBetweenEyes[0]]},$1={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},Ee={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function _t(e,t,n,A){for(let _=0;_<Yt.length;_++){let{key:c,indices:r}=Yt[_],o=ie[`${n}${c}`];if(!A||A.includes(c))for(let s=0;s<r.length;s++){let i=r[s];e[o[s]]=[t[i][0],t[i][1],(t[i][2]+e[o[s]][2])/2]}}}var Kt=class{constructor(t,n,A){var _,c;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=A,this.boxSize=((_=t==null?void 0:t.model)==null?void 0:_.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((c=t==null?void 0:t.model)==null?void 0:c.inputs[0].shape[2]),this.irisSize=(A==null?void 0:A.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,A,_){let c=Ne({startPoint:n.startPoint,endPoint:n.endPoint}),r=t.map(h=>[c[0]/this.meshSize*(h[0]-this.meshSize/2),c[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=A!==0?ct(A,[0,0]):rt,s=A!==0?r.map(h=>[...Q1(h,o),h[2]]):r,i=A!==0?G1(_):rt,a=[...Pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(h=>[h[0]+ye(a,i[0]),h[1]+ye(a,i[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ze.leftBounds[0]][2],A=t[ze.rightBounds[0]][2];return n-A}getEyeBox(t,n,A,_,c=!1){let r=nt(tt(this.calculateLandmarksBoundingBox([t[A],t[_]]),this.irisEnlarge)),o=Ne(r),s=B.image.cropAndResize(n,[[r.startPoint[1]/this.meshSize,r.startPoint[0]/this.meshSize,r.endPoint[1]/this.meshSize,r.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return c&&B.ENV.flags.IS_BROWSER&&(s=B.image.flipLeftRight(s)),{box:r,boxSize:o,crop:s}}getEyeCoords(t,n,A,_=!1){let c=[];for(let r=0;r<Ee.numCoordinates;r++){let o=t[r*3],s=t[r*3+1],i=t[r*3+2];c.push([(_?1-o/this.irisSize:o/this.irisSize)*A[0]+n.startPoint[0],s/this.irisSize*A[1]+n.startPoint[1],i])}return{rawCoords:c,iris:c.slice(Ee.index)}}getAdjustedIrisCoords(t,n,A){let _=t[ie[`${A}EyeUpper0`][Ee.upperCenter]][2],c=t[ie[`${A}EyeLower0`][Ee.lowerCenter]][2],r=(_+c)/2;return n.map((o,s)=>{let i=r;return s===2?i=_:s===4&&(i=c),[o[0],o[1],i]})}async predict(t,n){let A=!1,_;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(_=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||_&&_.boxes&&(!n.face.mesh.enabled||_.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let r of _.boxes)this.storedBoxes.push({startPoint:r.box.startPoint.dataSync(),endPoint:r.box.endPoint.dataSync(),landmarks:r.landmarks,confidence:r.confidence});this.storedBoxes.length>0&&(A=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),A){if(!_||!_.boxes||_.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let r=0;r<this.storedBoxes.length;r++){let o=C1({startPoint:this.storedBoxes[r].startPoint,endPoint:this.storedBoxes[r].endPoint},_.scaleFactor),s=tt(o),i=nt(s),a=this.storedBoxes[r].landmarks.arraySync(),h=this.storedBoxes[r].confidence;this.storedBoxes[r]={...i,confidence:h,landmarks:a}}}_&&_.boxes&&_.boxes.forEach(r=>{r.box.startPoint.dispose(),r.box.endPoint.dispose(),r.landmarks.dispose()});let c=B.tidy(()=>this.storedBoxes.map((r,o)=>{let s=r.confidence,i,a=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=At.count?At.symmetryLine:$1.symmetryLine;a=Ut(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=ct(-a,b),n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{h=rt;let u=t.clone();n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:r,faceConfidence:null,boxConfidence:s,confidence:r.confidence,image:i};let[,y,d]=this.meshDetector.predict(i),p=y.dataSync()[0];if(p<n.face.detector.minConfidence)return null;let f=B.reshape(d,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:u,boxSize:w,crop:b}=this.getEyeBox(f,i,ze.leftBounds[0],ze.leftBounds[1],!0),{box:M,boxSize:R,crop:H}=this.getEyeBox(f,i,ze.rightBounds[0],ze.rightBounds[1]),D=this.irisModel.predict(B.concat([b,H])).dataSync(),Pt=D.slice(0,Ee.numCoordinates*3),{rawCoords:Fe,iris:me}=this.getEyeCoords(Pt,u,w,!0),B0=D.slice(Ee.numCoordinates*3),{rawCoords:O1,iris:U0}=this.getEyeCoords(B0,M,R),k1=this.getLeftToRightEyeDepthDifference(f);Math.abs(k1)<30?(_t(f,Fe,"left",null),_t(f,O1,"right",null)):k1<1?_t(f,Fe,"left",["EyeUpper0","EyeLower0"]):_t(f,O1,"right",["EyeUpper0","EyeLower0"]);let Y0=this.getAdjustedIrisCoords(f,me,"left"),J0=this.getAdjustedIrisCoords(f,U0,"right");f=f.concat(Y0).concat(J0)}let m=this.transformRawCoords(f,r,a,h);r=tt(this.calculateLandmarksBoundingBox(m),1.5);let T=B.tensor2d(m);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=At.count?At.symmetryLine:$1.symmetryLine;a=Ut(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=ct(-a,b),i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let v={coords:T,box:r,faceConfidence:p,boxConfidence:s,image:i,rawCoords:f},k=nt(r);return this.storedBoxes[o]={...k,landmarks:m,confidence:r.confidence,faceConfidence:p},v}));return c=c.filter(r=>r!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(r=>r.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(t){let n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c,landmarks:t}}};var Qt=class{constructor(t,n,A,_){this.facePipeline=new Kt(t,n,A),this.config=_}async estimateFaces(t,n){let A=await this.facePipeline.predict(t,n),_=[];for(let c of A||[]){if(c.isDisposedInternal)continue;let r=c.coords?c.coords.arraySync():[],o=r.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),s={};if(r&&r.length>0)for(let h of Object.keys(ie))s[h]=ie[h].map(y=>r[y]);let i=c.box?[Math.max(0,c.box.startPoint[0]),Math.max(0,c.box.startPoint[1]),Math.min(t.shape[2],c.box.endPoint[0])-Math.max(0,c.box.startPoint[0]),Math.min(t.shape[1],c.box.endPoint[1])-Math.max(0,c.box.startPoint[1])]:0,a=c.box?[c.box.startPoint[0]/t.shape[2],c.box.startPoint[1]/t.shape[1],(c.box.endPoint[0]-c.box.startPoint[0])/t.shape[2],(c.box.endPoint[1]-c.box.startPoint[1])/t.shape[1]]:[];_.push({confidence:Math.round(100*c.faceConfidence||100*c.boxConfidence||0)/100,boxConfidence:Math.round(100*c.boxConfidence)/100,faceConfidence:Math.round(100*c.faceConfidence)/100,box:i,boxRaw:a,mesh:r,meshRaw:o,annotations:s,image:c.image?c.image.clone():null}),c.coords&&c.coords.dispose(),c.image&&c.image.dispose()}return _}},X=[null,null,null];async function $t(e){return!X[0]&&e.face.enabled||!X[1]&&e.face.mesh.enabled||!X[2]&&e.face.iris.enabled?(X=await Promise.all([!X[0]&&e.face.enabled?Y1(e):null,!X[1]&&e.face.mesh.enabled?Gt.loadGraphModel(W(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!X[2]&&e.face.iris.enabled?Gt.loadGraphModel(W(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!X[1]||!X[1].modelUrl?l("load model failed:",e.face.mesh.modelPath):e.debug&&l("load model:",X[1].modelUrl)),e.face.iris.enabled&&(!X[2]||!X[1].modelUrl?l("load model failed:",e.face.iris.modelPath):e.debug&&l("load model:",X[2].modelUrl))):e.debug&&(l("cached model:",X[0].model.modelUrl),l("cached model:",X[1].modelUrl),l("cached model:",X[2].modelUrl)),new Qt(X[0],X[1],X[2],e)}var e0=ue,t0=Oe;var h1={};ce(h1,{PoseNet:()=>i1,load:()=>a1});var p0=L(I());var n0=L(I());function pn(e){let[t,n,A,_]=e;return{offsets:t,heatmap:n,displacementFwd:A,displacementBwd:_}}var e1=class{constructor(t){this.model=t}predict(t){return n0.tidy(()=>{let A=t.toFloat().div(127.5).sub(1).expandDims(0),c=this.model.predict(A).map(o=>o.squeeze([0])),r=pn(c);return{heatmapScores:r.heatmap.sigmoid(),offsets:r.offsets,displacementFwd:r.displacementFwd,displacementBwd:r.displacementBwd}})}dispose(){this.model.dispose()}};function t1(e){return Math.floor(e/2)}var n1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(t1(t),t);)this.exchange(t,t1(t)),t=t1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=A}};function un(e,t,n,A,_,c){let[r,o]=c.shape,s=!0,i=Math.max(n-_,0),a=Math.min(n+_+1,r);for(let h=i;h<a;++h){let y=Math.max(A-_,0),d=Math.min(A+_+1,o);for(let p=y;p<d;++p)if(c.get(h,p,e)>t){s=!1;break}if(!s)break}return s}function r0(e,t,n){let[A,_,c]=n.shape,r=new n1(A*_*c,({score:o})=>o);for(let o=0;o<A;++o)for(let s=0;s<_;++s)for(let i=0;i<c;++i){let a=n.get(o,s,i);a<e||un(i,a,o,s,t,n)&&r.enqueue({score:a,part:{heatmapY:o,heatmapX:s,id:i}})}return r}var ve=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Re=ve.length,ke=ve.reduce((e,t,n)=>(e[t]=n,e),{}),bn=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Lr=bn.map(([e,t])=>[ke[e],ke[t]]),c0=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function r1(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function ot(e,t,n){let{heatmapY:A,heatmapX:_,id:c}=e,{y:r,x:o}=r1(A,_,c,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+r}}function c1(e,t,n){return e<t?t:e>n?n:e}function A0(e,t,n,A){let _=n-e,c=A-t;return _*_+c*c}function A1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var ne=L(I());function _0(e,t){let n=t.shape[0],A=new Float32Array(n);for(let _=0;_<n;_++){let c=t.get(_,0),r=t.get(_,1);A[_]=e.get(c,r,_)}return A}function gn(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function Tn(e,t){let n=[];for(let A=0;A<Re;A++){let _=e.get(A,0).valueOf(),c=e.get(A,1).valueOf(),{x:r,y:o}=gn(_,c,A,t);n.push(o),n.push(r)}return ne.tensor2d(n,[Re,2])}function o0(e,t,n){return ne.tidy(()=>e.toTensor().mul(ne.scalar(t,"int32")).toFloat().add(Tn(e,n)))}function Pn(e,t){return ne.tidy(()=>{let n=e.div(ne.scalar(t,"int32"));return e.sub(n.mul(ne.scalar(t,"int32")))})}function s0(e){let[t,n,A]=e.shape;return ne.tidy(()=>{let c=e.reshape([t*n,A]).argMax(0),r=c.div(ne.scalar(n,"int32")).expandDims(1),o=Pn(c,n).expandDims(1);return ne.concat([r,o],1)})}var h0=c0.map(([e,t])=>[ke[e],ke[t]]),_1=h0.map(([,e])=>e),i0=h0.map(([e])=>e),Mn=16;function zn(e,t,n){let A=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,A+e)}}function o1(e,t,n,A){return{y:c1(Math.round(e.y/t),0,n-1),x:c1(Math.round(e.x/t),0,A-1)}}function a0(e,t,n,A,_,c,r,o=2){let[s,i]=A.shape,a=o1(t.position,c,s,i),h=zn(e,a,r),d=A1(t.position,h);for(let f=0;f<o;f++){let m=o1(d,c,s,i),T=r1(m.y,m.x,n,_);d=A1({x:m.x*c,y:m.y*c},{x:T.x,y:T.y})}let p=o1(d,c,s,i),x=A.get(p.y,p.x,n);return{position:d,part:ve[n],score:x}}function x0(e,t,n,A,_,c){let r=t.shape[2],o=_1.length,s=new Array(r),{part:i,score:a}=e,h=ot(i,A,n);s[i.id]={score:a,part:ve[i.id],position:h};for(let y=o-1;y>=0;--y){let d=_1[y],p=i0[y];s[d]&&!s[p]&&(s[p]=a0(y,s[d],p,t,n,A,c))}for(let y=0;y<o;++y){let d=i0[y],p=_1[y];s[d]&&!s[p]&&(s[p]=a0(y,s[d],p,t,n,A,_))}return s}async function y0(e,t,n){let A=0,_=s0(e),c=await Promise.all([e.buffer(),t.buffer(),_.buffer()]),r=c[0],o=c[1],s=c[2],i=o0(s,Mn,o),a=await i.buffer(),y=Array.from(_0(r,s)).map((p,x)=>(A+=p,{position:{y:a.get(x,0),x:a.get(x,1)},part:ve[x],score:p})),d=y.filter(p=>p.score>n);return _.dispose(),i.dispose(),{keypoints:d,score:A/y.length}}var En=1,w0=16;function l0(e,t,{x:n,y:A},_){return e.some(({keypoints:c})=>{let r=c[_].position;return A0(A,n,r.y,r.x)<=t})}function vn(e,t,n){return n.reduce((_,{position:c,score:r},o)=>(l0(e,t,c,o)||(_+=r),_),0)/n.length}function d0(e,t,n,A,_,c,r){let o=[],s=r0(r,En,e),i=_^2;for(;o.length<c&&!s.empty();){let a=s.dequeue(),h=ot(a.part,w0,t);if(l0(o,i,h,a.part.id))continue;let y=x0(a,e,t,w0,n,A),d=vn(o,i,y);d>r&&o.push({keypoints:y,score:Math.round(100*d)/100})}return o}async function f0(e){return Promise.all(e.map(t=>t.buffer()))}function Rn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:A,part:_,position:c})=>({score:A,part:_,position:{x:Math.trunc(c.x*n),y:Math.trunc(c.y*t)}}))}}function m0(e,[t,n]){let A=e.squeeze(0),_=A.resizeBilinear([t,n]);return A.dispose(),_}function s1(e,[t,n],[A,_]){return e.map(r=>Rn(r,t/A,n/_))}var be;async function Sn(e,t,n,A){return new Promise(async _=>{let c=await f0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),r=c[0],o=c[1],s=c[2],i=c[3],a=await d0(r,o,s,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=s1(a,[e.shape[1],e.shape[2]],[A,A]);_(h)})}async function jn(e,t,n,A){return new Promise(async _=>{let c=await y0(t.heatmapScores,t.offsets,n.body.scoreThreshold),r=s1([c],[e.shape[1],e.shape[2]],[A,A]);_(r)})}var i1=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let A=m0(t,[this.inputSize,this.inputSize]),_=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await jn(t,_,n,this.inputSize):await Sn(t,_,n,this.inputSize);return _.heatmapScores.dispose(),_.offsets.dispose(),_.displacementFwd.dispose(),_.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function a1(e){be?e.debug&&l("cached model:",be.modelUrl):(be=await p0.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),!be||!be.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",be.modelUrl));let t=new e1(be);return new i1(t)}var d1={};ce(d1,{HandPose:()=>p1,load:()=>u1});var f1=L(I());var P=L(I());var u0=L(I());function st(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ie(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function b0(e,t,n){let 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n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c}}};var 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S0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],j0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var J;async function g1(e){return J?e.debug&&l("cached model:",J.modelUrl):(J=await le.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),J.width=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[2].size),J.height=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[1].size),!J||!J.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",J.modelUrl)),J}async function T1(e,t){if(!J||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},A=le.image.resizeBilinear(e,[J.width,J.height],!1),_=le.div(A,[255]);A.dispose();let c;if(t.profile){let a=await le.profile(()=>J.predict(_));c=a.result.find(h=>h.size===195||h.size===155).dataSync(),a.result.forEach(h=>h.dispose()),Y("blazepose",a)}else{let a=await J.predict(_);c=a.find(h=>h.size===195||h.size===155).dataSync(),a.forEach(h=>h.dispose())}_.dispose();let r=[],o=c.length===195?S0:j0,s=5;for(let 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dn=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],fn=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],mn=[33,133,362,263,1,78,308],br=dn.map(e=>Oe[e]),gr=fn.map(e=>Oe[e]),Tr=mn.map(e=>Oe[e]);var Jt=ie.leftEyeLower0,Ct=ie.rightEyeLower0,ze={leftBounds:[Jt[0],Jt[Jt.length-1]],rightBounds:[Ct[0],Ct[Ct.length-1]]},At={count:468,mouth:13,symmetryLine:[13,ie.midwayBetweenEyes[0]]},$1={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},ve={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function _t(e,t,n,A){for(let _=0;_<Yt.length;_++){let{key:c,indices:r}=Yt[_],o=ie[`${n}${c}`];if(!A||A.includes(c))for(let s=0;s<r.length;s++){let i=r[s];e[o[s]]=[t[i][0],t[i][1],(t[i][2]+e[o[s]][2])/2]}}}var Kt=class{constructor(t,n,A){var _,c;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=A,this.boxSize=((_=t==null?void 0:t.model)==null?void 0:_.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((c=t==null?void 0:t.model)==null?void 0:c.inputs[0].shape[2]),this.irisSize=(A==null?void 0:A.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,A,_){let c=Ne({startPoint:n.startPoint,endPoint:n.endPoint}),r=t.map(h=>[c[0]/this.meshSize*(h[0]-this.meshSize/2),c[1]/this.meshSize*(h[1]-this.meshSize/2),h[2]]),o=A!==0?ct(A,[0,0]):rt,s=A!==0?r.map(h=>[...Q1(h,o),h[2]]):r,i=A!==0?G1(_):rt,a=[...Pe({startPoint:n.startPoint,endPoint:n.endPoint}),1];return s.map(h=>[h[0]+ye(a,i[0]),h[1]+ye(a,i[1]),h[2]])}getLeftToRightEyeDepthDifference(t){let n=t[ze.leftBounds[0]][2],A=t[ze.rightBounds[0]][2];return n-A}getEyeBox(t,n,A,_,c=!1){let r=nt(tt(this.calculateLandmarksBoundingBox([t[A],t[_]]),this.irisEnlarge)),o=Ne(r),s=B.image.cropAndResize(n,[[r.startPoint[1]/this.meshSize,r.startPoint[0]/this.meshSize,r.endPoint[1]/this.meshSize,r.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return c&&B.ENV.flags.IS_BROWSER&&(s=B.image.flipLeftRight(s)),{box:r,boxSize:o,crop:s}}getEyeCoords(t,n,A,_=!1){let c=[];for(let r=0;r<ve.numCoordinates;r++){let o=t[r*3],s=t[r*3+1],i=t[r*3+2];c.push([(_?1-o/this.irisSize:o/this.irisSize)*A[0]+n.startPoint[0],s/this.irisSize*A[1]+n.startPoint[1],i])}return{rawCoords:c,iris:c.slice(ve.index)}}getAdjustedIrisCoords(t,n,A){let _=t[ie[`${A}EyeUpper0`][ve.upperCenter]][2],c=t[ie[`${A}EyeLower0`][ve.lowerCenter]][2],r=(_+c)/2;return n.map((o,s)=>{let i=r;return s===2?i=_:s===4&&(i=c),[o[0],o[1],i]})}async predict(t,n){let A=!1,_;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.videoOptimized)&&(_=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.videoOptimized&&this.skipped++,!n.videoOptimized||_&&_.boxes&&(!n.face.mesh.enabled||_.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let r of _.boxes)this.storedBoxes.push({startPoint:r.box.startPoint.dataSync(),endPoint:r.box.endPoint.dataSync(),landmarks:r.landmarks,confidence:r.confidence});this.storedBoxes.length>0&&(A=!0)}if(n.face.detector.skipInitial&&this.detectedFaces===0&&(this.skipped=0),A){if(!_||!_.boxes||_.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let r=0;r<this.storedBoxes.length;r++){let o=C1({startPoint:this.storedBoxes[r].startPoint,endPoint:this.storedBoxes[r].endPoint},_.scaleFactor),s=tt(o),i=nt(s),a=this.storedBoxes[r].landmarks.arraySync(),h=this.storedBoxes[r].confidence;this.storedBoxes[r]={...i,confidence:h,landmarks:a}}}_&&_.boxes&&_.boxes.forEach(r=>{r.box.startPoint.dispose(),r.box.endPoint.dispose(),r.landmarks.dispose()});let c=B.tidy(()=>this.storedBoxes.map((r,o)=>{let s=r.confidence,i,a=0,h;if(n.face.detector.rotation&&n.face.mesh.enabled&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=At.count?At.symmetryLine:$1.symmetryLine;a=Ut(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t,a,0,M);h=ct(-a,b),n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.boxSize,this.boxSize]).div(255)}else{h=rt;let u=t.clone();n.face.mesh.enabled?i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.meshSize,this.meshSize]).div(255):i=Me({startPoint:r.startPoint,endPoint:r.endPoint},u,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{coords:null,box:r,faceConfidence:null,boxConfidence:s,confidence:r.confidence,image:i};let[,y,d]=this.meshDetector.predict(i),p=y.dataSync()[0];if(p<n.face.detector.minConfidence)return null;let f=B.reshape(d,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:u,boxSize:w,crop:b}=this.getEyeBox(f,i,ze.leftBounds[0],ze.leftBounds[1],!0),{box:M,boxSize:R,crop:H}=this.getEyeBox(f,i,ze.rightBounds[0],ze.rightBounds[1]),D=this.irisModel.predict(B.concat([b,H])).dataSync(),Pt=D.slice(0,ve.numCoordinates*3),{rawCoords:Fe,iris:me}=this.getEyeCoords(Pt,u,w,!0),B0=D.slice(ve.numCoordinates*3),{rawCoords:O1,iris:U0}=this.getEyeCoords(B0,M,R),k1=this.getLeftToRightEyeDepthDifference(f);Math.abs(k1)<30?(_t(f,Fe,"left",null),_t(f,O1,"right",null)):k1<1?_t(f,Fe,"left",["EyeUpper0","EyeLower0"]):_t(f,O1,"right",["EyeUpper0","EyeLower0"]);let Y0=this.getAdjustedIrisCoords(f,me,"left"),J0=this.getAdjustedIrisCoords(f,U0,"right");f=f.concat(Y0).concat(J0)}let m=this.transformRawCoords(f,r,a,h);r=tt(this.calculateLandmarksBoundingBox(m),1.5);let T=B.tensor2d(m);if(n.face.detector.rotation&&n.face.mesh.enabled&&(n.face.description.enabled||n.face.embedding.enabled)&&B.ENV.flags.IS_BROWSER){let[u,w]=r.landmarks.length>=At.count?At.symmetryLine:$1.symmetryLine;a=Ut(r.landmarks[u],r.landmarks[w]);let b=Pe({startPoint:r.startPoint,endPoint:r.endPoint}),M=[b[0]/t.shape[2],b[1]/t.shape[1]],R=B.image.rotateWithOffset(t.toFloat(),a,0,M);h=ct(-a,b),i=Me({startPoint:r.startPoint,endPoint:r.endPoint},R,[this.meshSize,this.meshSize]).div(255)}let E={coords:T,box:r,faceConfidence:p,boxConfidence:s,image:i,rawCoords:f},k=nt(r);return this.storedBoxes[o]={...k,landmarks:m,confidence:r.confidence,faceConfidence:p},E}));return c=c.filter(r=>r!==null),n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(r=>r.faceConfidence>n.face.detector.minConfidence)),this.detectedFaces=c.length,c}calculateLandmarksBoundingBox(t){let n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c,landmarks:t}}};var Qt=class{constructor(t,n,A,_){this.facePipeline=new Kt(t,n,A),this.config=_}async estimateFaces(t,n){let A=await this.facePipeline.predict(t,n),_=[];for(let c of A||[]){if(c.isDisposedInternal)continue;let r=c.coords?c.coords.arraySync():[],o=r.map(h=>[h[0]/t.shape[2],h[1]/t.shape[1],h[2]/this.facePipeline.meshSize]),s={};if(r&&r.length>0)for(let h of Object.keys(ie))s[h]=ie[h].map(y=>r[y]);let i=c.box?[Math.max(0,c.box.startPoint[0]),Math.max(0,c.box.startPoint[1]),Math.min(t.shape[2],c.box.endPoint[0])-Math.max(0,c.box.startPoint[0]),Math.min(t.shape[1],c.box.endPoint[1])-Math.max(0,c.box.startPoint[1])]:0,a=c.box?[c.box.startPoint[0]/t.shape[2],c.box.startPoint[1]/t.shape[1],(c.box.endPoint[0]-c.box.startPoint[0])/t.shape[2],(c.box.endPoint[1]-c.box.startPoint[1])/t.shape[1]]:[];_.push({confidence:Math.round(100*c.faceConfidence||100*c.boxConfidence||0)/100,boxConfidence:Math.round(100*c.boxConfidence)/100,faceConfidence:Math.round(100*c.faceConfidence)/100,box:i,boxRaw:a,mesh:r,meshRaw:o,annotations:s,image:c.image?c.image.clone():null}),c.coords&&c.coords.dispose(),c.image&&c.image.dispose()}return _}},X=[null,null,null];async function $t(e){return!X[0]&&e.face.enabled||!X[1]&&e.face.mesh.enabled||!X[2]&&e.face.iris.enabled?(X=await Promise.all([!X[0]&&e.face.enabled?Y1(e):null,!X[1]&&e.face.mesh.enabled?Gt.loadGraphModel(W(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!X[2]&&e.face.iris.enabled?Gt.loadGraphModel(W(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!X[1]||!X[1].modelUrl?l("load model failed:",e.face.mesh.modelPath):e.debug&&l("load model:",X[1].modelUrl)),e.face.iris.enabled&&(!X[2]||!X[1].modelUrl?l("load model failed:",e.face.iris.modelPath):e.debug&&l("load model:",X[2].modelUrl))):e.debug&&(l("cached model:",X[0].model.modelUrl),l("cached model:",X[1].modelUrl),l("cached model:",X[2].modelUrl)),new Qt(X[0],X[1],X[2],e)}var e0=ue,t0=Oe;var h1={};ce(h1,{PoseNet:()=>i1,load:()=>a1});var p0=L(I());var n0=L(I());function pn(e){let[t,n,A,_]=e;return{offsets:t,heatmap:n,displacementFwd:A,displacementBwd:_}}var e1=class{constructor(t){this.model=t}predict(t){return n0.tidy(()=>{let A=t.toFloat().div(127.5).sub(1).expandDims(0),c=this.model.predict(A).map(o=>o.squeeze([0])),r=pn(c);return{heatmapScores:r.heatmap.sigmoid(),offsets:r.offsets,displacementFwd:r.displacementFwd,displacementBwd:r.displacementBwd}})}dispose(){this.model.dispose()}};function t1(e){return Math.floor(e/2)}var n1=class{constructor(t,n){this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=n}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(t1(t),t);)this.exchange(t,t1(t)),t=t1(t)}sink(t){for(;2*t<=this.numberOfElements;){let n=2*t;if(n<this.numberOfElements&&this.less(n,n+1)&&n++,!this.less(t,n))break;this.exchange(t,n),t=n}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,n){return this.getValueAt(t)<this.getValueAt(n)}exchange(t,n){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[n],this.priorityQueue[n]=A}};function un(e,t,n,A,_,c){let[r,o]=c.shape,s=!0,i=Math.max(n-_,0),a=Math.min(n+_+1,r);for(let h=i;h<a;++h){let y=Math.max(A-_,0),d=Math.min(A+_+1,o);for(let p=y;p<d;++p)if(c.get(h,p,e)>t){s=!1;break}if(!s)break}return s}function r0(e,t,n){let[A,_,c]=n.shape,r=new n1(A*_*c,({score:o})=>o);for(let o=0;o<A;++o)for(let s=0;s<_;++s)for(let i=0;i<c;++i){let a=n.get(o,s,i);a<e||un(i,a,o,s,t,n)&&r.enqueue({score:a,part:{heatmapY:o,heatmapX:s,id:i}})}return r}var Ee=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Re=Ee.length,ke=Ee.reduce((e,t,n)=>(e[t]=n,e),{}),bn=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Lr=bn.map(([e,t])=>[ke[e],ke[t]]),c0=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function r1(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function ot(e,t,n){let{heatmapY:A,heatmapX:_,id:c}=e,{y:r,x:o}=r1(A,_,c,n);return{x:e.heatmapX*t+o,y:e.heatmapY*t+r}}function c1(e,t,n){return e<t?t:e>n?n:e}function A0(e,t,n,A){let _=n-e,c=A-t;return _*_+c*c}function A1(e,t){return{x:e.x+t.x,y:e.y+t.y}}var ne=L(I());function _0(e,t){let n=t.shape[0],A=new Float32Array(n);for(let _=0;_<n;_++){let c=t.get(_,0),r=t.get(_,1);A[_]=e.get(c,r,_)}return A}function gn(e,t,n,A){return{y:A.get(e,t,n),x:A.get(e,t,n+Re)}}function Tn(e,t){let n=[];for(let A=0;A<Re;A++){let _=e.get(A,0).valueOf(),c=e.get(A,1).valueOf(),{x:r,y:o}=gn(_,c,A,t);n.push(o),n.push(r)}return ne.tensor2d(n,[Re,2])}function o0(e,t,n){return ne.tidy(()=>e.toTensor().mul(ne.scalar(t,"int32")).toFloat().add(Tn(e,n)))}function Pn(e,t){return ne.tidy(()=>{let n=e.div(ne.scalar(t,"int32"));return e.sub(n.mul(ne.scalar(t,"int32")))})}function s0(e){let[t,n,A]=e.shape;return ne.tidy(()=>{let c=e.reshape([t*n,A]).argMax(0),r=c.div(ne.scalar(n,"int32")).expandDims(1),o=Pn(c,n).expandDims(1);return ne.concat([r,o],1)})}var h0=c0.map(([e,t])=>[ke[e],ke[t]]),_1=h0.map(([,e])=>e),i0=h0.map(([e])=>e),Mn=16;function zn(e,t,n){let A=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,A+e)}}function o1(e,t,n,A){return{y:c1(Math.round(e.y/t),0,n-1),x:c1(Math.round(e.x/t),0,A-1)}}function a0(e,t,n,A,_,c,r,o=2){let[s,i]=A.shape,a=o1(t.position,c,s,i),h=zn(e,a,r),d=A1(t.position,h);for(let f=0;f<o;f++){let m=o1(d,c,s,i),T=r1(m.y,m.x,n,_);d=A1({x:m.x*c,y:m.y*c},{x:T.x,y:T.y})}let p=o1(d,c,s,i),x=A.get(p.y,p.x,n);return{position:d,part:Ee[n],score:x}}function x0(e,t,n,A,_,c){let r=t.shape[2],o=_1.length,s=new Array(r),{part:i,score:a}=e,h=ot(i,A,n);s[i.id]={score:a,part:Ee[i.id],position:h};for(let y=o-1;y>=0;--y){let d=_1[y],p=i0[y];s[d]&&!s[p]&&(s[p]=a0(y,s[d],p,t,n,A,c))}for(let y=0;y<o;++y){let d=i0[y],p=_1[y];s[d]&&!s[p]&&(s[p]=a0(y,s[d],p,t,n,A,_))}return s}async function y0(e,t,n){let A=0,_=s0(e),c=await Promise.all([e.buffer(),t.buffer(),_.buffer()]),r=c[0],o=c[1],s=c[2],i=o0(s,Mn,o),a=await i.buffer(),y=Array.from(_0(r,s)).map((p,x)=>(A+=p,{position:{y:a.get(x,0),x:a.get(x,1)},part:Ee[x],score:p})),d=y.filter(p=>p.score>n);return _.dispose(),i.dispose(),{keypoints:d,score:A/y.length}}var vn=1,w0=16;function l0(e,t,{x:n,y:A},_){return e.some(({keypoints:c})=>{let r=c[_].position;return A0(A,n,r.y,r.x)<=t})}function En(e,t,n){return n.reduce((_,{position:c,score:r},o)=>(l0(e,t,c,o)||(_+=r),_),0)/n.length}function d0(e,t,n,A,_,c,r){let o=[],s=r0(r,vn,e),i=_^2;for(;o.length<c&&!s.empty();){let a=s.dequeue(),h=ot(a.part,w0,t);if(l0(o,i,h,a.part.id))continue;let y=x0(a,e,t,w0,n,A),d=En(o,i,y);d>r&&o.push({keypoints:y,score:Math.round(100*d)/100})}return o}async function f0(e){return Promise.all(e.map(t=>t.buffer()))}function Rn(e,t,n){return{score:e.score,keypoints:e.keypoints.map(({score:A,part:_,position:c})=>({score:A,part:_,position:{x:Math.trunc(c.x*n),y:Math.trunc(c.y*t)}}))}}function m0(e,[t,n]){let A=e.squeeze(0),_=A.resizeBilinear([t,n]);return A.dispose(),_}function s1(e,[t,n],[A,_]){return e.map(r=>Rn(r,t/A,n/_))}var be;async function Sn(e,t,n,A){return new Promise(async _=>{let c=await f0([t.heatmapScores,t.offsets,t.displacementFwd,t.displacementBwd]),r=c[0],o=c[1],s=c[2],i=c[3],a=await d0(r,o,s,i,n.body.nmsRadius,n.body.maxDetections,n.body.scoreThreshold),h=s1(a,[e.shape[1],e.shape[2]],[A,A]);_(h)})}async function jn(e,t,n,A){return new Promise(async _=>{let c=await y0(t.heatmapScores,t.offsets,n.body.scoreThreshold),r=s1([c],[e.shape[1],e.shape[2]],[A,A]);_(r)})}var i1=class{constructor(t){this.baseModel=t,this.inputSize=t.model.inputs[0].shape[1],this.inputSize<128&&(this.inputSize=257)}async estimatePoses(t,n){let A=m0(t,[this.inputSize,this.inputSize]),_=this.baseModel.predict(A,n),c=n.body.maxDetections<2?await jn(t,_,n,this.inputSize):await Sn(t,_,n,this.inputSize);return _.heatmapScores.dispose(),_.offsets.dispose(),_.displacementFwd.dispose(),_.displacementBwd.dispose(),A.dispose(),c}dispose(){this.baseModel.dispose()}};async function a1(e){be?e.debug&&l("cached model:",be.modelUrl):(be=await p0.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),!be||!be.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",be.modelUrl));let t=new e1(be);return new i1(t)}var d1={};ce(d1,{HandPose:()=>p1,load:()=>u1});var f1=L(I());var P=L(I());var u0=L(I());function st(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Ie(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function b0(e,t,n){let 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n=t.map(r=>r[0]),A=t.map(r=>r[1]),_=[Math.min(...n),Math.min(...A)],c=[Math.max(...n),Math.max(...A)];return{startPoint:_,endPoint:c}}};var 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S0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],j0=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var J;async function g1(e){return J?e.debug&&l("cached model:",J.modelUrl):(J=await le.loadGraphModel(W(e.modelBasePath,e.body.modelPath)),J.width=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[2].size),J.height=parseInt(J.signature.inputs["input_1:0"].tensorShape.dim[1].size),!J||!J.modelUrl?l("load model failed:",e.body.modelPath):e.debug&&l("load model:",J.modelUrl)),J}async function T1(e,t){if(!J||!t.body.enabled)return null;let n={width:e.shape[2],height:e.shape[1]},A=le.image.resizeBilinear(e,[J.width,J.height],!1),_=le.div(A,[255]);A.dispose();let c;if(t.profile){let a=await le.profile(()=>J.predict(_));c=a.result.find(h=>h.size===195||h.size===155).dataSync(),a.result.forEach(h=>h.dispose()),Y("blazepose",a)}else{let a=await J.predict(_);c=a.find(h=>h.size===195||h.size===155).dataSync(),a.forEach(h=>h.dispose())}_.dispose();let r=[],o=c.length===195?S0:j0,s=5;for(let 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cannot set backend:",this.config.backend,_)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(l("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let _=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&l(`gl version:${_.getParameter(_.VERSION)} renderer:${_.getParameter(_.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(E()-A)}});bt.set(this,async()=>{let t=(_,c="application/octet-stream")=>fetch(`data:${c};base64,${_}`).then(r=>r.blob()),n,A;switch(this.config.warmup){case"face":n=await t(mt);break;case"full":n=await t(pt);break;default:n=null}if(n){let _=await createImageBitmap(n);A=await this.detect(_,this.config),_.close()}return A});gt.set(this,async()=>new Promise(t=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+mt;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+pt;break;default:n=null}let _=new Image;_.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=_.naturalWidth,c.height=_.naturalHeight;let r=c.getContext("2d");r==null||r.drawImage(_,0,0);let o=await this.detect(c,this.config);t(o)},n?_.src=n:t(null)}));Tt.set(this,async()=>{var r;if(typeof je.node=="undefined")return this.config.debug&&l("Warmup tfjs-node not loaded"),null;let t=o=>Buffer.from(o,"base64"),n;if(this.config.warmup==="face"&&(n=t(mt)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(pt)),!n)return null;let A=(r=je.node)==null?void 0:r.decodeJpeg(n),_=A.expandDims(0);this.tf.dispose(A);let c=await this.detect(_,this.config);return this.tf.dispose(_),c});this.tf=je,this.draw=W1,this.version=F0,this.config=ee(j,t),this.state="idle",pe(this,We,0),pe(this,He,!1),pe(this,Ve,!1),pe(this,Te,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>j1(n,this.config),this.classes={facemesh:Dt,age:Et,gender:Rt,emotion:Ot,faceres:Vt,body:this.config.body.modelPath.includes("posenet")?h1:b1,hand:d1,nanodet:z1},this.faceTriangulation=e0,this.faceUVMap=t0,this.sysinfo=Z1()}profileData(){return this.config.profile?Be:{}}similarity(t,n){return this.config.face.description.enabled?Ft(t,n):this.config.face.embedding.enabled?F1(t,n):0}enhance(t){return qt(t)}match(t,n,A=0){return q1(t,n,A)}async load(t={}){this.state="load";let n=E();t&&(this.config=ee(this.config,t)),Ae(this,Te)&&(this.config.debug&&l(`version: ${this.version}`),this.config.debug&&l(`tfjs version: ${this.tf.version_core}`),this.config.debug&&l("platform:",this.sysinfo.platform),this.config.debug&&l("agent:",this.sysinfo.agent),await Ae(this,Xe).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&l("configuration:",this.config),this.config.debug&&l("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?$t(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?vt(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Nt(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Lt(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Zt(this.config):null),this.models.handpose||(this.config.hand.enabled?u1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?a1(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?g1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?P1(this.config):null),this.models.nanodet||(this.config.object.enabled?v1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Xt(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await $t(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await vt(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Nt(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Lt(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Zt(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await u1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await a1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await g1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await P1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await v1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Xt(this.config))),Ae(this,Te)&&(this.config.debug&&l("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),pe(this,Te,!1));let A=Math.trunc(E()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async detect(t,n={}){return new Promise(async A=>{var f,m,T,v;this.state="config";let _;this.config=ee(this.config,n),this.state="check";let c=Ae(this,ut).call(this,t);c&&(l(c,t),A({error:c}));let r=E();await Ae(this,Xe).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&S1 instanceof ImageBitmap||t instanceof je.Tensor)&&(l("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),_=E();let s=j1(t,this.config);if(!s||!s.tensor){l("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(E()-_),this.analyze("Get Image:");let i,a,h,y,d;this.config.async?(h=this.config.face.enabled?Bt(this,s.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",_=E(),h=this.config.face.enabled?await Bt(this,s.tensor):[],d=Math.trunc(E()-_),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?(f=this.models.posenet)==null?void 0:f.estimatePoses(s.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?T1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?M1(s.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",_=E(),this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?await((m=this.models.posenet)==null?void 0:m.estimatePoses(s.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?await T1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?await M1(s.tensor,this.config):[]),d=Math.trunc(E()-_),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(a=this.config.hand.enabled?(T=this.models.handpose)==null?void 0:T.estimateHands(s.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",_=E(),a=this.config.hand.enabled?await((v=this.models.handpose)==null?void 0:v.estimateHands(s.tensor,this.config)):[],d=Math.trunc(E()-_),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(y=this.config.object.enabled?R1(s.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",_=E(),y=this.config.object.enabled?await R1(s.tensor,this.config):[],d=Math.trunc(E()-_),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([h,i,a,y]=await Promise.all([h,i,a,y])),je.dispose(s.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(_=E(),p=[...N0(h),...W0(i),...k0(a),...O0(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(E()-_)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(E()-r),this.state="idle";let x={face:h,body:i,hand:a,gesture:p,object:y,performance:this.perf,canvas:s.canvas};A(x)})}async warmup(t={}){let n=E();if(t&&(this.config=ee(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let A=this.config.videoOptimized;this.config.videoOptimized=!1;let _;typeof createImageBitmap=="function"?_=await Ae(this,bt).call(this):typeof Image!="undefined"?_=await Ae(this,gt).call(this):_=await Ae(this,Tt).call(this),this.config.videoOptimized=A;let c=E();return this.config.debug&&l("Warmup",this.config.warmup,Math.round(c-n),"ms",_),_}};We=new WeakMap,He=new WeakMap,Ve=new WeakMap,Te=new WeakMap,ut=new WeakMap,Xe=new WeakMap,bt=new WeakMap,gt=new WeakMap,Tt=new WeakMap;0&&(module.exports={Human});
+2Q==`;var F0="1.5.1";var We,He,Ve,Te,ut,Xe,bt,gt,Tt,q0=class{constructor(t={}){We.set(this,void 0);He.set(this,void 0);Ve.set(this,void 0);Te.set(this,void 0);this.analyze=(...t)=>{if(!Ae(this,He))return;let n=this.tf.engine().state.numTensors,A=Ae(this,We);pe(this,We,n);let _=n-A;_!==0&&l(...t,_)};ut.set(this,t=>{if(!Ae(this,Ve))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof je.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});Xe.set(this,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let A=v();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&l("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&l("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let _=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),c=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&l(`wasm execution: ${_?"SIMD":"no SIMD"} ${c?"multithreaded":"singlethreaded"}`),this.config.debug&&!_&&l("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&X1();try{await this.tf.setBackend(this.config.backend)}catch(_){l("error: cannot set backend:",this.config.backend,_)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.deallocate&&(l("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1));let _=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&l(`gl version:${_.getParameter(_.VERSION)} renderer:${_.getParameter(_.RENDERER)}`)}await this.tf.ready(),this.perf.backend=Math.trunc(v()-A)}});bt.set(this,async()=>{let t=(_,c="application/octet-stream")=>fetch(`data:${c};base64,${_}`).then(r=>r.blob()),n,A;switch(this.config.warmup){case"face":n=await t(mt);break;case"full":n=await t(pt);break;default:n=null}if(n){let _=await createImageBitmap(n);A=await this.detect(_,this.config),_.close()}return A});gt.set(this,async()=>new Promise(t=>{let n,A=0;switch(this.config.warmup){case"face":A=256,n="data:image/jpeg;base64,"+mt;break;case"full":case"body":A=1200,n="data:image/jpeg;base64,"+pt;break;default:n=null}let _=new Image;_.onload=async()=>{let c=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(A,A):document.createElement("canvas");c.width=_.naturalWidth,c.height=_.naturalHeight;let r=c.getContext("2d");r==null||r.drawImage(_,0,0);let o=await this.detect(c,this.config);t(o)},n?_.src=n:t(null)}));Tt.set(this,async()=>{let t=_=>Buffer.from(_,"base64"),n;if(this.config.warmup==="face"&&(n=t(mt)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(pt)),!n)return null;let A;if(typeof je.node!="undefined"){let _=je.node.decodeJpeg(n),c=_.expandDims(0);this.tf.dispose(_),A=await this.detect(c,this.config),this.tf.dispose(c)}else this.config.debug&&l("Warmup tfjs-node not loaded");return A});this.tf=je,this.draw=W1,this.version=F0,this.config=ee(j,t),this.state="idle",pe(this,We,0),pe(this,He,!1),pe(this,Ve,!1),pe(this,Te,!0),this.perf={},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,handpose:null,iris:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,faceres:null},this.image=n=>j1(n,this.config),this.classes={facemesh:Dt,age:vt,gender:Rt,emotion:Ot,faceres:Vt,body:this.config.body.modelPath.includes("posenet")?h1:b1,hand:d1,nanodet:z1},this.faceTriangulation=e0,this.faceUVMap=t0,this.sysinfo=Z1()}profileData(){return this.config.profile?Be:{}}similarity(t,n){return this.config.face.description.enabled?Ft(t,n):this.config.face.embedding.enabled?F1(t,n):0}enhance(t){return qt(t)}match(t,n,A=0){return q1(t,n,A)}async load(t={}){this.state="load";let n=v();t&&(this.config=ee(this.config,t)),Ae(this,Te)&&(this.config.debug&&l(`version: ${this.version}`),this.config.debug&&l(`tfjs version: ${this.tf.version_core}`),this.config.debug&&l("platform:",this.sysinfo.platform),this.config.debug&&l("agent:",this.sysinfo.agent),await Ae(this,Xe).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&l("configuration:",this.config),this.config.debug&&l("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.nanodet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?$t(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?Et(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?Nt(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?Lt(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?Zt(this.config):null),this.models.handpose||(this.config.hand.enabled?u1(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?a1(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?g1(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?P1(this.config):null),this.models.nanodet||(this.config.object.enabled?E1(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?Xt(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await $t(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await Et(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await Nt(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await Lt(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await Zt(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await u1(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await a1(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await g1(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await P1(this.config)),this.config.object.enabled&&!this.models.nanodet&&(this.models.nanodet=await E1(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await Xt(this.config))),Ae(this,Te)&&(this.config.debug&&l("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),pe(this,Te,!1));let A=Math.trunc(v()-n);A>(this.perf.load||0)&&(this.perf.load=A)}async detect(t,n={}){return new Promise(async A=>{var f,m,T,E;this.state="config";let _;this.config=ee(this.config,n),this.state="check";let c=Ae(this,ut).call(this,t);c&&(l(c,t),A({error:c}));let r=v();await Ae(this,Xe).call(this),await this.load(),this.config.scoped&&this.tf.engine().startScope(),this.analyze("Start Scope:");let o;t&&this.config.videoOptimized&&(t instanceof HTMLImageElement||t instanceof Image||t instanceof ImageData||typeof ImageBitmap!="undefined"&&S1 instanceof ImageBitmap||t instanceof je.Tensor)&&(l("disabling video optimization"),o=this.config.videoOptimized,this.config.videoOptimized=!1),_=v();let s=j1(t,this.config);if(!s||!s.tensor){l("could not convert input to tensor"),A({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(v()-_),this.analyze("Get Image:");let i,a,h,y,d;this.config.async?(h=this.config.face.enabled?Bt(this,s.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",_=v(),h=this.config.face.enabled?await Bt(this,s.tensor):[],d=Math.trunc(v()-_),d>0&&(this.perf.face=d)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?(f=this.models.posenet)==null?void 0:f.estimatePoses(s.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?T1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?M1(s.tensor,this.config):[]),this.perf.body&&delete this.perf.body):(this.state="run:body",_=v(),this.config.body.modelPath.includes("posenet")?i=this.config.body.enabled?await((m=this.models.posenet)==null?void 0:m.estimatePoses(s.tensor,this.config)):[]:this.config.body.modelPath.includes("blazepose")?i=this.config.body.enabled?await T1(s.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")&&(i=this.config.body.enabled?await M1(s.tensor,this.config):[]),d=Math.trunc(v()-_),d>0&&(this.perf.body=d)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(a=this.config.hand.enabled?(T=this.models.handpose)==null?void 0:T.estimateHands(s.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",_=v(),a=this.config.hand.enabled?await((E=this.models.handpose)==null?void 0:E.estimateHands(s.tensor,this.config)):[],d=Math.trunc(v()-_),d>0&&(this.perf.hand=d)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(y=this.config.object.enabled?R1(s.tensor,this.config):[],this.perf.object&&delete this.perf.object):(this.state="run:object",_=v(),y=this.config.object.enabled?await R1(s.tensor,this.config):[],d=Math.trunc(v()-_),d>0&&(this.perf.object=d)),this.analyze("End Object:"),this.config.async&&([h,i,a,y]=await Promise.all([h,i,a,y])),je.dispose(s.tensor),this.config.scoped&&this.tf.engine().endScope(),this.analyze("End Scope:");let p=[];this.config.gesture.enabled&&(_=v(),p=[...N0(h),...W0(i),...k0(a),...O0(h)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(v()-_)),o&&(this.config.videoOptimized=o),this.perf.total=Math.trunc(v()-r),this.state="idle";let x={face:h,body:i,hand:a,gesture:p,object:y,performance:this.perf,canvas:s.canvas};A(x)})}async warmup(t={}){let n=v();if(t&&(this.config=ee(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let A=this.config.videoOptimized;this.config.videoOptimized=!1;let _;typeof createImageBitmap=="function"?_=await Ae(this,bt).call(this):typeof Image!="undefined"?_=await Ae(this,gt).call(this):_=await Ae(this,Tt).call(this),this.config.videoOptimized=A;let c=v();return this.config.debug&&l("Warmup",this.config.warmup,Math.round(c-n),"ms",_),_}};We=new WeakMap,He=new WeakMap,Ve=new WeakMap,Te=new WeakMap,ut=new WeakMap,Xe=new WeakMap,bt=new WeakMap,gt=new WeakMap,Tt=new WeakMap;0&&(module.exports={Human});
 //# sourceMappingURL=human.node.js.map
diff --git a/dist/human.node.js.map b/dist/human.node.js.map
index bedc4743..9b912f71 100644
--- a/dist/human.node.js.map
+++ b/dist/human.node.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/tfjs/tf-node.ts", "../src/human.ts", "../src/helpers.ts", "../src/sysinfo.ts", "../src/tfjs/backend.ts", "../src/age/age.ts", "../src/profile.ts", "../src/gender/gender.ts", "../src/emotion/emotion.ts", "../src/embedding/embedding.ts", "../src/faceres/faceres.ts", "../src/faceall.ts", "../src/blazeface/facemesh.ts", "../src/blazeface/blazeface.ts", "../src/blazeface/facepipeline.ts", "../src/blazeface/box.ts", "../src/blazeface/util.ts", "../src/blazeface/coords.ts", "../src/posenet/posenet.ts", "../src/posenet/modelBase.ts", "../src/posenet/heapSort.ts", "../src/posenet/buildParts.ts", "../src/posenet/keypoints.ts", "../src/posenet/vectors.ts", "../src/posenet/decoders.ts", "../src/posenet/decodePose.ts", "../src/posenet/decodeMultiple.ts", "../src/posenet/util.ts", "../src/handpose/handpose.ts", "../src/handpose/handdetector.ts", "../src/handpose/box.ts", "../src/handpose/handpipeline.ts", "../src/handpose/util.ts", "../src/handpose/anchors.ts", "../src/blazepose/blazepose.ts", "../src/blazepose/annotations.ts", "../src/efficientpose/efficientpose.ts", "../src/nanodet/nanodet.ts", "../src/nanodet/labels.ts", "../src/gesture/gesture.ts", "../src/image/image.ts", "../src/image/imagefx.js", "../src/draw/draw.ts", "../src/config.ts", "../src/sample.ts"],
-  "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore // typescript cannot infer type\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        // @ts-ignore // typescript cannot infer type\n        this.models.handpose,\n        // @ts-ignore // typescript cannot infer type\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (!simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    // @ts-ignore\n    if (typeof tf.node === 'undefined') {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      return null;\n    }\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    // @ts-ignore // tf.node is only defined when compiling for nodejs\n    const data = tf.node?.decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    this.tf.dispose(data);\n    // log('Input:', expanded);\n    const res = await this.detect(expanded, this.config);\n    this.tf.dispose(expanded);\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 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0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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+  "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log, now, mergeDeep } from './helpers';\nimport * as sysinfo from './sysinfo';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend';\nimport * as faceall from './faceall';\nimport * as facemesh from './blazeface/facemesh';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as faceres from './faceres/faceres';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as posenet from './posenet/posenet';\nimport * as handpose from './handpose/handpose';\nimport * as blazepose from './blazepose/blazepose';\nimport * as efficientpose from './efficientpose/efficientpose';\nimport * as nanodet from './nanodet/nanodet';\nimport * as gesture from './gesture/gesture';\nimport * as image from './image/image';\nimport * as draw from './draw/draw';\nimport * as profile from './profile';\nimport { Config, defaults } from './config';\nimport { Result } from './result';\nimport * as sample from './sample';\nimport * as app from '../package.json';\n\n/** Generic Tensor object type */\nexport type Tensor = typeof tf.Tensor;\n\nexport type { Config } from './config';\nexport type { Result } from './result';\nexport type { DrawOptions } from './draw/draw';\n\n/** Defines all possible input types for **Human** detection */\nexport type Input = Tensor | typeof Image | ImageData | ImageBitmap | HTMLImageElement | HTMLMediaElement | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;\n\n/** Error message */\nexport type Error = { error: string };\n\n/** Instance of TensorFlow/JS */\nexport type TensorFlow = typeof tf;\n\n/** Generic Model object type, holds instance of individual models */\ntype Model = Object;\n\n/**\n * **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n */\nexport class Human {\n  /** Current version of Human library in semver format */\n  version: string;\n  /** Current configuration\n   * - Details: {@link Config}\n   */\n  config: Config;\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   */\n  state: string;\n  /** Internal: Instance of current image being processed */\n  image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };\n  /** Internal: Instance of TensorFlow/JS used by Human\n   * - Can be embedded or externally provided\n   */\n  tf: TensorFlow;\n  /** Draw helper classes that can draw detected objects on canvas using specified draw styles\n   * - options: global settings for all draw operations, can be overriden for each draw method, for details see {@link DrawOptions}\n   * - face: draw detected faces\n   * - body: draw detected people and body parts\n   * - hand: draw detected hands and hand parts\n   * - canvas: draw processed canvas which is a processed copy of the input\n   * - all: meta-function that performs: canvas, face, body, hand\n   */\n  draw: {\n    options: draw.DrawOptions,\n    gesture: typeof draw.gesture,\n    face: typeof draw.face,\n    body: typeof draw.body,\n    hand: typeof draw.hand,\n    canvas: typeof draw.canvas,\n    all: typeof draw.all,\n  };\n  /** Internal: Currently loaded models */\n  models: {\n    face: facemesh.MediaPipeFaceMesh | Model | null,\n    posenet: posenet.PoseNet | null,\n    blazepose: Model | null,\n    efficientpose: Model | null,\n    handpose: handpose.HandPose | null,\n    iris: Model | null,\n    age: Model | null,\n    gender: Model | null,\n    emotion: Model | null,\n    embedding: Model | null,\n    nanodet: Model | null,\n    faceres: Model | null,\n  };\n  /** Internal: Currently loaded classes */\n  classes: {\n    facemesh: typeof facemesh;\n    age: typeof age;\n    gender: typeof gender;\n    emotion: typeof emotion;\n    body: typeof posenet | typeof blazepose;\n    hand: typeof handpose;\n    nanodet: typeof nanodet;\n    faceres: typeof faceres;\n  };\n  /** Face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: typeof facemesh.triangulation;\n  /** UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: typeof facemesh.uvmap;\n  /** Platform and agent information detected by Human */\n  sysinfo: { platform: string, agent: string };\n  /** Performance object that contains values for all recently performed operations */\n  perf: any;\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  #firstRun: boolean;\n\n  // definition end\n\n  /**\n   * Creates instance of Human library that is futher used for all operations\n   * - @param userConfig: {@link Config}\n   */\n  constructor(userConfig: Config | Object = {}) {\n    this.tf = tf;\n    this.draw = draw;\n    this.version = app.version;\n    this.config = mergeDeep(defaults, userConfig);\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.#firstRun = true;\n    this.perf = {};\n    // object that contains all initialized models\n    this.models = {\n      face: null,\n      posenet: null,\n      blazepose: null,\n      efficientpose: null,\n      handpose: null,\n      iris: null,\n      age: null,\n      gender: null,\n      emotion: null,\n      embedding: null,\n      nanodet: null,\n      faceres: null,\n    };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.image = (input: Input) => image.process(input, this.config);\n    // export raw access to underlying models\n    this.classes = {\n      facemesh,\n      age,\n      gender,\n      emotion,\n      faceres,\n      body: this.config.body.modelPath.includes('posenet') ? posenet : blazepose,\n      hand: handpose,\n      nanodet,\n    };\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // include platform info\n    this.sysinfo = sysinfo.info();\n  }\n\n  /** Internal: ProfileData method returns last known profiling information\n   * - Requires human.config.profile set to true\n   */\n  profileData(): { newBytes, newTensors, peakBytes, numKernelOps, timeKernelOps, slowestKernelOps, largestKernelOps } | {} {\n    if (this.config.profile) return profile.data;\n    return {};\n  }\n\n  // helper function: measure tensor leak\n  /** @hidden */\n  analyze = (...msg) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const current = this.tf.engine().state.numTensors;\n    const previous = this.#numTensors;\n    this.#numTensors = current;\n    const leaked = current - previous;\n    if (leaked !== 0) log(...msg, leaked);\n  }\n\n  // quick sanity check on inputs\n  /** @hidden */\n  #sanity = (input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  }\n\n  /** Simmilarity method calculates simmilarity between two provided face descriptors (face embeddings)\n   * - Calculation is based on normalized Minkowski distance between\n  */\n  similarity(embedding1: Array<number>, embedding2: Array<number>): number {\n    if (this.config.face.description.enabled) return faceres.similarity(embedding1, embedding2);\n    if (this.config.face.embedding.enabled) return embedding.similarity(embedding1, embedding2);\n    return 0;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   * @param input Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /**\n   * Math method find best match between provided face descriptor and predefined database of known descriptors\n   * @param faceEmbedding: face descriptor previsouly calculated on any face\n   * @param db: array of mapping of face descriptors to known values\n   * @param threshold: minimum score for matching to be considered in the result\n   * @returns best match\n   */\n  // eslint-disable-next-line class-methods-use-this\n  match(faceEmbedding: Array<number>, db: Array<{ name: string, source: string, embedding: number[] }>, threshold = 0): { name: string, source: string, similarity: number, embedding: number[] } {\n    return faceres.match(faceEmbedding, db, threshold);\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n  */\n  async load(userConfig: Config | Object = {}) {\n    this.state = 'load';\n    const timeStamp = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n    if (this.#firstRun) {\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version_core}`);\n      if (this.config.debug) log('platform:', this.sysinfo.platform);\n      if (this.config.debug) log('agent:', this.sysinfo.agent);\n\n      await this.#checkBackend(true);\n      if (this.tf.ENV.flags.IS_BROWSER) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n    if (this.config.async) {\n      [\n        this.models.face,\n        this.models.age,\n        this.models.gender,\n        this.models.emotion,\n        this.models.embedding,\n        this.models.handpose,\n        this.models.posenet,\n        this.models.blazepose,\n        this.models.efficientpose,\n        this.models.nanodet,\n        this.models.faceres,\n      ] = await Promise.all([\n        this.models.face || (this.config.face.enabled ? facemesh.load(this.config) : null),\n        this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n        this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n        this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n        this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n        this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? <any>posenet.load(this.config) : null),\n        this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),\n        this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),\n        this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),\n        this.models.faceres || ((this.config.face.enabled && this.config.face.description.enabled) ? faceres.load(this.config) : null),\n      ]);\n    } else {\n      if (this.config.face.enabled && !this.models.face) this.models.face = await facemesh.load(this.config);\n      if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n      if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n      if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n      if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n      if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelPath.includes('posenet')) this.models.posenet = await posenet.load(this.config);\n      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelPath.includes('blazepose')) this.models.blazepose = await blazepose.load(this.config);\n      if (this.config.body.enabled && !this.models.efficientpose && this.config.body.modelPath.includes('efficientpose')) this.models.efficientpose = await efficientpose.load(this.config);\n      if (this.config.object.enabled && !this.models.nanodet) this.models.nanodet = await nanodet.load(this.config);\n      if (this.config.face.enabled && this.config.face.description.enabled && !this.models.faceres) this.models.faceres = await faceres.load(this.config);\n    }\n\n    if (this.#firstRun) {\n      if (this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors');\n      this.#firstRun = false;\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.perf.load || 0)) this.perf.load = current;\n  }\n\n  // check if backend needs initialization if it changed\n  /** @hidden */\n  #checkBackend = async (force = false) => {\n    if (this.config.backend && (this.config.backend.length > 0) && force || (this.tf.getBackend() !== this.config.backend)) {\n      const timeStamp = now();\n      this.state = 'backend';\n      /* force backend reload\n      if (this.config.backend in tf.engine().registry) {\n        const backendFactory = tf.findBackendFactory(this.config.backend);\n        tf.removeBackend(this.config.backend);\n        tf.registerBackend(this.config.backend, backendFactory);\n      } else {\n        log('Backend not registred:', this.config.backend);\n      }\n      */\n\n      if (this.config.backend && this.config.backend.length > 0) {\n        // force browser vs node backend\n        if (this.tf.ENV.flags.IS_BROWSER && this.config.backend === 'tensorflow') this.config.backend = 'webgl';\n        if (this.tf.ENV.flags.IS_NODE && (this.config.backend === 'webgl' || this.config.backend === 'humangl')) this.config.backend = 'tensorflow';\n\n        if (this.config.debug) log('setting backend:', this.config.backend);\n\n        if (this.config.backend === 'wasm') {\n          if (this.config.debug) log('wasm path:', this.config.wasmPath);\n          if (typeof this.tf?.setWasmPaths !== 'undefined') this.tf.setWasmPaths(this.config.wasmPath);\n          else throw new Error('Human: WASM backend is not loaded');\n          const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');\n        }\n\n        if (this.config.backend === 'humangl') backend.register();\n        try {\n          await this.tf.setBackend(this.config.backend);\n        } catch (err) {\n          log('error: cannot set backend:', this.config.backend, err);\n        }\n      }\n      this.tf.enableProdMode();\n      // this.tf.enableDebugMode();\n      if (this.tf.getBackend() === 'webgl' || this.tf.getBackend() === 'humangl') {\n        this.tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n        this.tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n        if (this.config.deallocate) {\n          log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n          this.tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n        }\n        const gl = await this.tf.backend().getGPGPUContext().gl;\n        if (this.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n      await this.tf.ready();\n      this.perf.backend = Math.trunc(now() - timeStamp);\n    }\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n  */\n  async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {\n    // detection happens inside a promise\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig);\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        resolve({ error });\n      }\n\n      const timeStart = now();\n\n      // configure backend\n      await this.#checkBackend();\n\n      // load models if enabled\n      await this.load();\n\n      if (this.config.scoped) this.tf.engine().startScope();\n      this.analyze('Start Scope:');\n\n      // disable video optimization for inputs of type image\n      let previousVideoOptimized;\n      if (input && this.config.videoOptimized && (input instanceof HTMLImageElement || input instanceof Image || input instanceof ImageData || (typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap) || input instanceof tf.Tensor)) {\n        log('disabling video optimization');\n        previousVideoOptimized = this.config.videoOptimized;\n        this.config.videoOptimized = false;\n      }\n\n      timeStamp = now();\n      const process = image.process(input, this.config);\n      if (!process || !process.tensor) {\n        log('could not convert input to tensor');\n        resolve({ error: 'could not convert input to tensor' });\n        return;\n      }\n      this.perf.image = Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      // prepare where to store model results\n      let bodyRes;\n      let handRes;\n      let faceRes;\n      let objectRes;\n      let current;\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? faceall.detectFace(this, process.tensor) : [];\n        if (this.perf.face) delete this.perf.face;\n      } else {\n        this.state = 'run:face';\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await faceall.detectFace(this, process.tensor) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.face = current;\n      }\n\n      // run body: can be posenet or blazepose\n      this.analyze('Start Body:');\n      if (this.config.async) {\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];\n        if (this.perf.body) delete this.perf.body;\n      } else {\n        this.state = 'run:body';\n        timeStamp = now();\n        if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n        else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.body = current;\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      if (this.config.async) {\n        handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        if (this.perf.hand) delete this.perf.hand;\n      } else {\n        this.state = 'run:hand';\n        timeStamp = now();\n        handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.hand = current;\n      }\n      this.analyze('End Hand:');\n\n      // run nanodet\n      this.analyze('Start Object:');\n      if (this.config.async) {\n        objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];\n        if (this.perf.object) delete this.perf.object;\n      } else {\n        this.state = 'run:object';\n        timeStamp = now();\n        objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];\n        current = Math.trunc(now() - timeStamp);\n        if (current > 0) this.perf.object = current;\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      if (this.config.async) {\n        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n      }\n      tf.dispose(process.tensor);\n\n      if (this.config.scoped) this.tf.engine().endScope();\n      this.analyze('End Scope:');\n\n      // run gesture analysis last\n      let gestureRes: any[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n        if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n        else if (this.perf.gesture) delete this.perf.gesture;\n      }\n\n      // restore video optimizations if previously disabled\n      if (previousVideoOptimized) this.config.videoOptimized = previousVideoOptimized;\n\n      this.perf.total = Math.trunc(now() - timeStart);\n      this.state = 'idle';\n      const result = {\n        face: faceRes,\n        body: bodyRes,\n        hand: handRes,\n        gesture: gestureRes,\n        object: objectRes,\n        performance: this.perf,\n        canvas: process.canvas,\n      };\n      // log('Result:', result);\n      resolve(result);\n    });\n  }\n\n  /** @hidden */\n  #warmupBitmap = async () => {\n    const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n    let blob;\n    let res;\n    switch (this.config.warmup) {\n      case 'face': blob = await b64toBlob(sample.face); break;\n      case 'full': blob = await b64toBlob(sample.body); break;\n      default: blob = null;\n    }\n    if (blob) {\n      const bitmap = await createImageBitmap(blob);\n      res = await this.detect(bitmap, this.config);\n      bitmap.close();\n    }\n    return res;\n  }\n\n  /** @hidden */\n  #warmupCanvas = async () => new Promise((resolve) => {\n    let src;\n    let size = 0;\n    switch (this.config.warmup) {\n      case 'face':\n        size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    const img = new Image();\n    img.onload = async () => {\n      const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n      canvas.width = img.naturalWidth;\n      canvas.height = img.naturalHeight;\n      const ctx = canvas.getContext('2d');\n      ctx?.drawImage(img, 0, 0);\n      // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n      const res = await this.detect(canvas, this.config);\n      resolve(res);\n    };\n    if (src) img.src = src;\n    else resolve(null);\n  });\n\n  /** @hidden */\n  #warmupNode = async () => {\n    const atob = (str) => Buffer.from(str, 'base64');\n    let img;\n    if (this.config.warmup === 'face') img = atob(sample.face);\n    if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);\n    if (!img) return null;\n    let res;\n    if (typeof tf['node'] !== 'undefined') {\n      const data = tf['node'].decodeJpeg(img);\n      const expanded = data.expandDims(0);\n      this.tf.dispose(data);\n      // log('Input:', expanded);\n      res = await this.detect(expanded, this.config);\n      this.tf.dispose(expanded);\n    } else {\n      if (this.config.debug) log('Warmup tfjs-node not loaded');\n      /*\n      const input = await canvasJS.loadImage(img);\n      const canvas = canvasJS.createCanvas(input.width, input.height);\n      const ctx = canvas.getContext('2d');\n      ctx.drawImage(img, 0, 0, input.width, input.height);\n      res = await this.detect(input, this.config);\n      */\n    }\n    return res;\n  }\n\n  /** Warmup metho pre-initializes all models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {\n    const t0 = now();\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };\n    const save = this.config.videoOptimized;\n    this.config.videoOptimized = false;\n    let res;\n    if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();\n    else if (typeof Image !== 'undefined') res = await this.#warmupCanvas();\n    else res = await this.#warmupNode();\n    this.config.videoOptimized = save;\n    const t1 = now();\n    if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);\n    return res;\n  }\n}\n\n/**\n * Class Human is also available as default export\n */\nexport { Human as default };\n", "// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`Human: ModelPath Error: ${path} Expecting JSON file`);\n  return path;\n}\n\n// helper function: wrapper around console output\nexport function log(...msg) {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n", "export function info(): { platform: string, agent: string } {\n  let platform;\n  let agent;\n  if (typeof navigator !== 'undefined') {\n    const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n    if (raw && raw[0]) {\n      const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n      platform = platformMatch ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n      agent = navigator.userAgent.replace(raw[0], '');\n      if (platform[1]) agent = agent.replace(raw[1], '');\n      agent = agent.replace(/  /g, ' ');\n    }\n  } else if (typeof process !== 'undefined') {\n    platform = `${process.platform} ${process.arch}`;\n    agent = `NodeJS ${process.version}`;\n  }\n  return { platform, agent };\n}\n", "import { log } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n  name: 'humangl',\n  priority: 99,\n  canvas: <null | OffscreenCanvas | HTMLCanvasElement>null,\n  gl: <any>null,\n  width: 1024,\n  height: 1024,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nexport function register(): void {\n  if (!tf.findBackend(config.name)) {\n    log('backend registration:', config.name);\n    try {\n      config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n    } catch (err) {\n      log('error: cannot get WebGL2 context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL2 context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n      // tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));\n      // tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    log('backend registered:', config.name);\n  }\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.age.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.age.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const enhance = tf.mul(resize, [255.0]);\n    tf.dispose(resize);\n\n    let ageT;\n    const obj = { age: 0 };\n\n    if (!config.profile) {\n      if (config.face.age.enabled) ageT = await model.predict(enhance);\n    } else {\n      const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      ageT = profileAge.result.clone();\n      profileAge.result.dispose();\n      profile.run('age', profileAge);\n    }\n    enhance.dispose();\n\n    if (ageT) {\n      const data = ageT.dataSync();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    ageT.dispose();\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log } from './helpers';\n\nexport const data = {};\n\nexport function run(modelName: string, profileData: any): void {\n  if (!profileData || !profileData.kernels) return;\n  const maxResults = 5;\n  const time = profileData.kernels\n    .filter((a) => a.kernelTimeMs > 0)\n    .reduce((a, b) => a += b.kernelTimeMs, 0);\n  const slowest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.kernelTimeMs > 0)\n    .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n  const largest = profileData.kernels\n    .map((a, i) => { a.id = i; return a; })\n    .filter((a) => a.totalBytesSnapshot > 0)\n    .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n  if (slowest.length > maxResults) slowest.length = maxResults;\n  if (largest.length > maxResults) largest.length = maxResults;\n  data[modelName] = {\n    model: modelName,\n    newBytes: profileData.newBytes,\n    newTensors: profileData.newTensors,\n    peakBytes: profileData.peakBytes,\n    numKernelOps: profileData.kernels.length,\n    timeKernelOps: time,\n    slowestKernelOps: slowest,\n    largestKernelOps: largest,\n  };\n  log('profiler', modelName, data[modelName]);\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.gender.modelPath));\n    alternative = model.inputs[0].shape[3] === 1;\n    if (!model || !model.modelUrl) log('load model failed:', config.face.gender.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    let enhance;\n    if (alternative) {\n      enhance = tf.tidy(() => {\n        const [red, green, blue] = tf.split(resize, 3, 3);\n        const redNorm = tf.mul(red, rgb[0]);\n        const greenNorm = tf.mul(green, rgb[1]);\n        const blueNorm = tf.mul(blue, rgb[2]);\n        const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n        const normalize = grayscale.sub(0.5).mul(2); // range grayscale:-1..1\n        return normalize;\n      });\n    } else {\n      enhance = tf.mul(resize, [255.0]); // range RGB:0..255\n    }\n    tf.dispose(resize);\n\n    let genderT;\n    const obj = { gender: '', confidence: 0 };\n\n    if (!config.profile) {\n      if (config.face.gender.enabled) genderT = await model.predict(enhance);\n    } else {\n      const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};\n      genderT = profileGender.result.clone();\n      profileGender.result.dispose();\n      profile.run('gender', profileGender);\n    }\n    enhance.dispose();\n\n    if (genderT) {\n      if (!Array.isArray(genderT)) {\n        const data = genderT.dataSync();\n        if (alternative) {\n          // returns two values 0..1, bigger one is prediction\n          if (data[0] > config.face.gender.minConfidence || data[1] > config.face.gender.minConfidence) {\n            obj.gender = data[0] > data[1] ? 'female' : 'male';\n            obj.confidence = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n          }\n        } else {\n          // returns one value 0..1, .5 is prediction threshold\n          const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n          if (confidence > config.face.gender.minConfidence) {\n            obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n            obj.confidence = Math.min(0.99, confidence);\n          }\n        }\n        genderT.dispose();\n      } else {\n        const gender = genderT[0].dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.confidence = Math.min(0.99, confidence);\n        }\n        /*\n        let age = genderT[1].argMax(1).dataSync()[0];\n        const all = genderT[1].dataSync();\n        age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n        const descriptor = genderT[1].dataSync();\n        */\n        genderT.forEach((t) => tf.dispose(t));\n      }\n    }\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model;\nlet last: Array<{ score: number, emotion: string }> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    const [red, green, blue] = tf.split(resize, 3, 3);\n    resize.dispose();\n    // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    red.dispose();\n    green.dispose();\n    blue.dispose();\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    redNorm.dispose();\n    greenNorm.dispose();\n    blueNorm.dispose();\n    const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n    grayscale.dispose();\n    const obj: Array<{ score: number, emotion: string }> = [];\n    if (config.face.emotion.enabled) {\n      let data;\n      if (!config.profile) {\n        const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation\n        data = emotionT.dataSync();\n        tf.dispose(emotionT);\n      } else {\n        const profileData = await tf.profile(() => model.predict(normalize));\n        data = profileData.result.dataSync();\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });\n      }\n      obj.sort((a, b) => b.score - a.score);\n    }\n    normalize.dispose();\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.embedding.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.embedding.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(Math.trunc(1000 * (1 - distance)) / 1000, 0);\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    // const data = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    const tensor = input.image || input.tensor;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    // normalize brightness from 0..1\n    const darken = merge.sub(merge.min());\n    const lighten = darken.div(darken.max());\n\n    return lighten;\n  });\n  return image;\n}\n\nexport async function predict(input, config): Promise<number[]> {\n  if (!model) return [];\n  return new Promise(async (resolve) => {\n    // let data: Array<[]> = [];\n    let data: Array<number> = [];\n    if (config.face.embedding.enabled) {\n      const image = enhance(input);\n      if (!config.profile) {\n        data = tf.tidy(() => {\n          /*\n          // if needed convert from NHWC to NCHW\n          const nchw = image.transpose([3, 0, 1, 2]);\n          */\n\n          const res = model.predict(image);\n\n          /*\n          // optionally do it twice with flipped image and average results\n          const res1 = model.predict(image);\n          const flipped = tf.image.flipLeftRight(image);\n          const res2 = model.predict(flipped);\n          const merge = tf.stack([res1, res2], 2).squeeze();\n          const res = reshape.logSumExp(1);\n          */\n\n          /*\n          // optional normalize outputs with l2 normalization\n          const scaled = tf.tidy(() => {\n            const l2 = res.norm('euclidean');\n            const scale = res.div(l2);\n            return scale;\n          });\n          */\n\n          // optional reduce feature vector complexity\n          const reshape = res.reshape([128, 2]); // split 256 vectors into 128 x 2\n          const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n          const output: Array<number> = reduce.dataSync();\n          return [...output]; // convert typed array to simple array\n        });\n      } else {\n        const profileData = await tf.profile(() => model.predict({ img_inputs: image }));\n        data = [...profileData.result.dataSync()];\n        profileData.result.dispose();\n        profile.run('emotion', profileData);\n      }\n      tf.dispose(image);\n    }\n    resolve(data);\n  });\n}\n\n/*\ngit clone https://github.com/becauseofAI/MobileFace\ncd MobileFace/MobileFace_Identification\nmmconvert --srcFramework mxnet --inputWeight MobileFace_Identification_V3-0000.params --inputNetwork MobileFace_Identification_V3-symbol.json --inputShape 3,112,112 --dstFramework tensorflow --outputModel saved\nsaved_model_cli show --dir saved/\ntensorflowjs_converter --input_format tf_saved_model --output_format tfjs_graph_model --saved_model_tags train saved/ graph/\n~/dev/detector/signature.js graph/\n2021-03-12 08:25:12 DATA:  created on: 2021-03-12T13:17:11.960Z\n2021-03-12 08:25:12 INFO:  graph model: /home/vlado/dev/face/MobileFace/MobileFace_Identification/graph/model.json\n2021-03-12 08:25:12 INFO:  size: { unreliable: true, numTensors: 75, numDataBuffers: 75, numBytes: 2183192 }\n2021-03-12 08:25:12 INFO:  model inputs based on signature\n2021-03-12 08:25:12 INFO:  model outputs based on signature\n2021-03-12 08:25:12 DATA:  inputs: [ { name: 'data:0', dtype: 'DT_FLOAT', shape: [ -1, 112, 112, 3, [length]: 4 ] }, [length]: 1 ]\n2021-03-12 08:25:12 DATA:  outputs: [ { id: 0, name: 'batchnorm0/add_1:0', dytpe: 'DT_FLOAT', shape: [ -1, 256, [length]: 2 ] }, [length]: 1 ]\n*/\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\ntype Tensor = typeof tf.Tensor;\ntype DB = Array<{ name: string, source: string, embedding: number[] }>;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.face.description.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.face.description.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport function similarity(embedding1, embedding2, order = 2): number {\n  if (!embedding1 || !embedding2) return 0;\n  if (embedding1?.length === 0 || embedding2?.length === 0) return 0;\n  if (embedding1?.length !== embedding2?.length) return 0;\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  const distance = 5.0 * embedding1\n    .map((val, i) => (Math.abs(embedding1[i] - embedding2[i]) ** order)) // distance squared\n    .reduce((sum, now) => (sum + now), 0) // sum all distances\n    ** (1 / order); // get root of\n  const res = Math.max(0, 100 - distance) / 100.0;\n  return res;\n}\n\nexport function match(embedding: Array<number>, db: DB, threshold = 0) {\n  let best = { similarity: 0, name: '', source: '', embedding: [] as number[] };\n  if (!embedding || !db || !Array.isArray(embedding) || !Array.isArray(db)) return best;\n  for (const f of db) {\n    if (f.embedding && f.name) {\n      const perc = similarity(embedding, f.embedding);\n      if (perc > threshold && perc > best.similarity) best = { ...f, similarity: perc };\n    }\n  }\n  return best;\n}\n\nexport function enhance(input): Tensor {\n  const image = tf.tidy(() => {\n    // input received from detector is already normalized to 0..1\n    // input is also assumed to be straightened\n    const tensor = input.image || input.tensor || input;\n    if (!(tensor instanceof tf.Tensor)) return null;\n    // do a tight crop of image and resize it to fit the model\n    const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n    // const box = [[0.0, 0.0, 1.0, 1.0]]; // basically no crop for test\n    const crop = (tensor.shape.length === 3)\n      ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n      : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    // const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n\n    /*\n    // convert to black&white to avoid colorization impact\n    const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n    const [red, green, blue] = tf.split(crop, 3, 3);\n    const redNorm = tf.mul(red, rgb[0]);\n    const greenNorm = tf.mul(green, rgb[1]);\n    const blueNorm = tf.mul(blue, rgb[2]);\n    const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n    const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n    */\n\n    /*\n    // optional increase image contrast\n    // or do it per-channel so mean is done on each channel\n    // or do it based on histogram\n    const mean = merge.mean();\n    const factor = 5;\n    const contrast = merge.sub(mean).mul(factor).add(mean);\n    */\n\n    /*\n    // normalize brightness from 0..1\n    const darken = crop.sub(crop.min());\n    const lighten = darken.div(darken.max());\n    */\n\n    const norm = crop.mul(255);\n\n    return norm;\n  });\n  return image;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.face.description.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const enhanced = enhance(image);\n\n    let resT;\n    const obj = {\n      age: <number>0,\n      gender: <string>'unknown',\n      genderConfidence: <number>0,\n      descriptor: <number[]>[] };\n\n    if (!config.profile) {\n      if (config.face.description.enabled) resT = await model.predict(enhanced);\n    } else {\n      const profileDesc = config.face.description.enabled ? await tf.profile(() => model.predict(enhanced)) : {};\n      resT = profileDesc.result;\n      profile.run('faceres', profileDesc);\n    }\n    tf.dispose(enhanced);\n\n    if (resT) {\n      tf.tidy(() => {\n        const gender = resT.find((t) => t.shape[1] === 1).dataSync();\n        const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n        if (confidence > config.face.gender.minConfidence) {\n          obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n          obj.genderConfidence = Math.min(0.99, confidence);\n        }\n        const age = resT.find((t) => t.shape[1] === 100).argMax(1).dataSync()[0];\n        const all = resT.find((t) => t.shape[1] === 100).dataSync();\n        obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n        const desc = resT.find((t) => t.shape[1] === 1024);\n        // const reshape = desc.reshape([128, 8]);\n        // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n\n        obj.descriptor = [...desc.dataSync()];\n      });\n      resT.forEach((t) => tf.dispose(t));\n    }\n\n    last = obj;\n    resolve(obj);\n  });\n}\n", "import { log, now } from './helpers';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as age from './age/age';\nimport * as gender from './gender/gender';\nimport * as emotion from './emotion/emotion';\nimport * as embedding from './embedding/embedding';\nimport * as faceres from './faceres/faceres';\n\ntype Tensor = typeof tf.Tensor;\n\nconst calculateFaceAngle = (face, image_size): { angle: { pitch: number, yaw: number, roll: number }, matrix: [number, number, number, number, number, number, number, number, number] } => {\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const degrees = (theta) => (theta * 180) / Math.PI;\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v) => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a, b) => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a, b) => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r) => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, r01, r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX; let thetaY; let thetaZ;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n  // simple Euler angle calculation based existing 3D mesh\n  // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n  const meshToEulerAngle = (mesh) => {\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const angle = {\n      // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees\n      // value of 0 means center\n      // pitch is face move up/down\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face\n      // yaw is face turn left/right\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye\n      // roll is face lean left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye\n    };\n    return angle;\n  };\n\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1] };\n\n  const size = Math.max(face.boxRaw[2] * image_size[0], face.boxRaw[3] * image_size[1]) / 1.5;\n  // top, bottom, left, right\n  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [\n    // make the xyz coordinates proportional, independent of the image/box size\n    pt[0] * image_size[0] / size,\n    pt[1] * image_size[1] / size,\n    pt[2],\n  ]);\n\n  const y_axis = normalize(subVectors(pts[1], pts[0]));\n  let x_axis = normalize(subVectors(pts[3], pts[2]));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n  return { angle, matrix };\n};\n\nexport const detectFace = async (parent, input): Promise<any> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp;\n  let ageRes;\n  let genderRes;\n  let emotionRes;\n  let embeddingRes;\n  let descRes;\n  const faceRes: Array<{\n      confidence: number,\n      boxConfidence: number,\n      faceConfidence: number,\n      box: [number, number, number, number],\n      mesh: Array<[number, number, number]>\n      meshRaw: Array<[number, number, number]>\n      boxRaw: [number, number, number, number],\n      annotations: Array<{ part: string, points: Array<[number, number, number]>[] }>,\n      age: number,\n      gender: string,\n      genderConfidence: number,\n      emotion: string,\n      embedding: number[],\n      iris: number,\n      rotation: {\n        angle: { pitch: number, yaw: number, roll: number },\n        matrix: [number, number, number, number, number, number, number, number, number]\n      },\n      tensor: Tensor,\n    }> = [];\n  parent.state = 'run:face';\n  timeStamp = now();\n  const faces = await parent.models.face?.estimateFaces(input, parent.config);\n  parent.perf.face = Math.trunc(now() - timeStamp);\n  if (!faces) return [];\n  for (const face of faces) {\n    parent.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    if (!face.image || face.image.isDisposedInternal) {\n      log('Face object is disposed:', face.image);\n      continue;\n    }\n\n    const rotation = calculateFaceAngle(face, [input.shape[2], input.shape[1]]);\n\n    // run age, inherits face from blazeface\n    parent.analyze('Start Age:');\n    if (parent.config.async) {\n      ageRes = parent.config.face.age.enabled ? age.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:age';\n      timeStamp = now();\n      ageRes = parent.config.face.age.enabled ? await age.predict(face.image, parent.config) : {};\n      parent.perf.age = Math.trunc(now() - timeStamp);\n    }\n\n    // run gender, inherits face from blazeface\n    parent.analyze('Start Gender:');\n    if (parent.config.async) {\n      genderRes = parent.config.face.gender.enabled ? gender.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:gender';\n      timeStamp = now();\n      genderRes = parent.config.face.gender.enabled ? await gender.predict(face.image, parent.config) : {};\n      parent.perf.gender = Math.trunc(now() - timeStamp);\n    }\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Emotion:');\n    if (parent.config.async) {\n      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(face.image, parent.config) : {};\n    } else {\n      parent.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(face.image, parent.config) : {};\n      parent.perf.emotion = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Emotion:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Embedding:');\n    if (parent.config.async) {\n      embeddingRes = parent.config.face.embedding.enabled ? embedding.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:embedding';\n      timeStamp = now();\n      embeddingRes = parent.config.face.embedding.enabled ? await embedding.predict(face, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Embedding:');\n\n    // run emotion, inherits face from blazeface\n    parent.analyze('Start Description:');\n    if (parent.config.async) {\n      descRes = parent.config.face.description.enabled ? faceres.predict(face, parent.config) : [];\n    } else {\n      parent.state = 'run:description';\n      timeStamp = now();\n      descRes = parent.config.face.description.enabled ? await faceres.predict(face.image, parent.config) : [];\n      parent.perf.embedding = Math.trunc(now() - timeStamp);\n    }\n    parent.analyze('End Description:');\n\n    // if async wait for results\n    if (parent.config.async) {\n      [ageRes, genderRes, emotionRes, embeddingRes, descRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes, descRes]);\n    }\n\n    parent.analyze('Finish Face:');\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!parent.config.face.iris.enabled && face?.annotations?.leftEyeIris && face?.annotations?.rightEyeIris) {\n      delete face.annotations.leftEyeIris;\n      delete face.annotations.rightEyeIris;\n    }\n    const irisSize = (face.annotations?.leftEyeIris && face.annotations?.rightEyeIris)\n    /* average human iris size is 11.7mm */\n      ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n      : 0;\n\n    // combine results\n    faceRes.push({\n      ...face,\n      age: descRes.age || ageRes.age,\n      gender: descRes.gender || genderRes.gender,\n      genderConfidence: descRes.genderConfidence || genderRes.confidence,\n      embedding: descRes.descriptor || embeddingRes,\n      emotion: emotionRes,\n      iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n      rotation,\n      tensor: parent.config.face.detector.return ? face.image?.squeeze() : null,\n    });\n    // dispose original face tensor\n    face.image?.dispose();\n\n    parent.analyze('End Face');\n  }\n  parent.analyze('End FaceMesh:');\n  if (parent.config.async) {\n    if (parent.perf.face) delete parent.perf.face;\n    if (parent.perf.age) delete parent.perf.age;\n    if (parent.perf.gender) delete parent.perf.gender;\n    if (parent.perf.emotion) delete parent.perf.emotion;\n  }\n  return faceRes;\n};\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as facepipeline from './facepipeline';\nimport * as coords from './coords';\n\nexport class MediaPipeFaceMesh {\n  facePipeline: any;\n  config: any;\n\n  constructor(blazeFace, blazeMeshModel, irisModel, config) {\n    this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);\n    this.config = config;\n  }\n\n  async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {\n    const predictions = await this.facePipeline.predict(input, config);\n    const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];\n    for (const prediction of (predictions || [])) {\n      if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n      const mesh = prediction.coords ? prediction.coords.arraySync() : [];\n      const meshRaw = mesh.map((pt) => [\n        pt[0] / input.shape[2],\n        pt[1] / input.shape[1],\n        pt[2] / this.facePipeline.meshSize,\n      ]);\n      const annotations = {};\n      if (mesh && mesh.length > 0) {\n        for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.startPoint[0]),\n        Math.max(0, prediction.box.startPoint[1]),\n        Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),\n        Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),\n      ] : 0;\n      const boxRaw = prediction.box ? [\n        prediction.box.startPoint[0] / input.shape[2],\n        prediction.box.startPoint[1] / input.shape[1],\n        (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],\n        (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],\n      ] : [];\n      results.push({\n        confidence: Math.round(100 * prediction.faceConfidence || 100 * prediction.boxConfidence || 0) / 100,\n        boxConfidence: Math.round(100 * prediction.boxConfidence) / 100,\n        faceConfidence: Math.round(100 * prediction.faceConfidence) / 100,\n        box,\n        boxRaw,\n        mesh,\n        meshRaw,\n        annotations,\n        image: prediction.image ? prediction.image.clone() : null,\n      });\n      if (prediction.coords) prediction.coords.dispose();\n      if (prediction.image) prediction.image.dispose();\n    }\n    return results;\n  }\n}\n\nlet faceModels:[any, any, any] = [null, null, null];\nexport async function load(config): Promise<MediaPipeFaceMesh> {\n  if ((!faceModels[0] && config.face.enabled) || (!faceModels[1] && config.face.mesh.enabled) || (!faceModels[2] && config.face.iris.enabled)) {\n    faceModels = await Promise.all([\n      (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n      (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.mesh.modelPath), { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n      (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(join(config.modelBasePath, config.face.iris.modelPath), { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.face.mesh.enabled) {\n      if (!faceModels[1] || !faceModels[1].modelUrl) log('load model failed:', config.face.mesh.modelPath);\n      else if (config.debug) log('load model:', faceModels[1].modelUrl);\n    }\n    if (config.face.iris.enabled) {\n      if (!faceModels[2] || !faceModels[1].modelUrl) log('load model failed:', config.face.iris.modelPath);\n      else if (config.debug) log('load model:', faceModels[2].modelUrl);\n    }\n  } else if (config.debug) {\n    log('cached model:', faceModels[0].model.modelUrl);\n    log('cached model:', faceModels[1].modelUrl);\n    log('cached model:', faceModels[2].modelUrl);\n  }\n  const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n  return faceMesh;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) {\n          anchors.push([anchorX, anchorY]);\n        }\n      }\n    }\n  }\n  return anchors;\n}\n\nexport const disposeBox = (box) => {\n  box.startEndTensor.dispose();\n  box.startPoint.dispose();\n  box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n  startEndTensor,\n  startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n  endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n  const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  const centers = tf.add(boxStarts, anchors);\n  const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  const boxSizesNormalized = tf.div(boxSizes, inputSize);\n  const centersNormalized = tf.div(centers, inputSize);\n  const halfBoxSize = tf.div(boxSizesNormalized, 2);\n  const starts = tf.sub(centersNormalized, halfBoxSize);\n  const ends = tf.add(centersNormalized, halfBoxSize);\n  const startNormalized = tf.mul(starts, inputSize);\n  const endNormalized = tf.mul(ends, inputSize);\n  const concatAxis = 1;\n  return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nexport class BlazeFaceModel {\n  model: any;\n  anchorsData: any;\n  anchors: any;\n  inputSize: number;\n  config: any;\n\n  constructor(model, config) {\n    this.model = model;\n    this.anchorsData = generateAnchors(model.inputs[0].shape[1]);\n    this.anchors = tf.tensor2d(this.anchorsData);\n    this.inputSize = model.inputs[0].shape[2];\n    this.config = config;\n  }\n\n  async getBoundingBoxes(inputImage) {\n    // sanity check on input\n    if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n    const [batch, boxes, scores] = tf.tidy(() => {\n      const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);\n      // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n      const normalizedImage = resizedImage.div(127.5).sub(0.5);\n      const batchedPrediction = this.model.predict(normalizedImage);\n      let batchOut;\n      // are we using tfhub or pinto converted model?\n      if (Array.isArray(batchedPrediction)) {\n        const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n        const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n        const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n        const concat = tf.concat([concat512, concat384], 1);\n        batchOut = concat.squeeze(0);\n      } else {\n        batchOut = batchedPrediction.squeeze(); // when using tfhub model\n      }\n      const boxesOut = decodeBounds(batchOut, this.anchors, [this.inputSize, this.inputSize]);\n      const logits = tf.slice(batchOut, [0, 0], [-1, 1]);\n      const scoresOut = tf.sigmoid(logits).squeeze();\n      return [batchOut, boxesOut, scoresOut];\n    });\n    const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n    const boxIndices = boxIndicesTensor.arraySync();\n    boxIndicesTensor.dispose();\n    const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n    const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n      const vals = boundingBox.arraySync();\n      boundingBox.dispose();\n      return vals;\n    });\n\n    const scoresVal = scores.dataSync();\n    const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];\n    for (let i = 0; i < boundingBoxes.length; i++) {\n      const boxIndex = boxIndices[i];\n      const confidence = scoresVal[boxIndex];\n      if (confidence > this.config.face.detector.minConfidence) {\n        const box = createBox(boundingBoxes[i]);\n        const anchor = this.anchorsData[boxIndex];\n        const landmarks = tf.tidy(() => tf.slice(batch, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n        annotatedBoxes.push({ box, landmarks, anchor, confidence });\n      }\n    }\n    batch.dispose();\n    boxes.dispose();\n    scores.dispose();\n    return {\n      boxes: annotatedBoxes,\n      scaleFactor: [inputImage.shape[2] / this.inputSize, inputImage.shape[1] / this.inputSize],\n    };\n  }\n}\n\nexport async function load(config) {\n  const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n  const blazeFace = new BlazeFaceModel(model, config);\n  if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);\n  else if (config.debug) log('load model:', model.modelUrl);\n  return blazeFace;\n}\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords';\n\nconst leftOutline = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst rightOutline = coords.MESH_ANNOTATIONS['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst meshLandmarks = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]],\n};\n\nconst blazeFaceLandmarks = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nexport class Pipeline {\n  storedBoxes: any;\n  boundingBoxDetector: any;\n  meshDetector: any;\n  irisModel: any;\n  boxSize: number;\n  meshSize: number;\n  irisSize: number;\n  irisEnlarge: number;\n  skipped: number;\n  detectedFaces: number;\n\n  constructor(boundingBoxDetector, meshDetector, irisModel) {\n    // An array of facial bounding boxes.\n    this.storedBoxes = [];\n    this.boundingBoxDetector = boundingBoxDetector;\n    this.meshDetector = meshDetector;\n    this.irisModel = irisModel;\n    this.boxSize = boundingBoxDetector?.model?.inputs[0].shape[2] || 0;\n    this.meshSize = meshDetector?.inputs[0].shape[2] || boundingBoxDetector?.model?.inputs[0].shape[2];\n    this.irisSize = irisModel?.inputs[0].shape[1] || 0;\n    this.irisEnlarge = 2.3;\n    this.skipped = 0;\n    this.detectedFaces = 0;\n  }\n\n  transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n    const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n    const coordsScaled = rawCoords.map((coord) => ([\n      boxSize[0] / this.meshSize * (coord[0] - this.meshSize / 2),\n      boxSize[1] / this.meshSize * (coord[1] - this.meshSize / 2),\n      coord[2],\n    ]));\n    const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n    const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n    const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n    const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n    return coordsRotated.map((coord) => ([\n      coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n      coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n      coord[2],\n    ]));\n  }\n\n  getLeftToRightEyeDepthDifference(rawCoords) {\n    const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n    const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n    return leftEyeZ - rightEyeZ;\n  }\n\n  // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n  getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n    const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n    const boxSize = bounding.getBoxSize(box);\n    let crop = tf.image.cropAndResize(face, [[\n      box.startPoint[1] / this.meshSize,\n      box.startPoint[0] / this.meshSize, box.endPoint[1] / this.meshSize,\n      box.endPoint[0] / this.meshSize,\n    ]], [0], [this.irisSize, this.irisSize]);\n    if (flip && tf.ENV.flags.IS_BROWSER) {\n      crop = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    }\n    return { box, boxSize, crop };\n  }\n\n  // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n  getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n    const eyeRawCoords: Array<any[]> = [];\n    for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n      const x = eyeData[i * 3];\n      const y = eyeData[i * 3 + 1];\n      const z = eyeData[i * 3 + 2];\n      eyeRawCoords.push([\n        (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n        (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n      ]);\n    }\n    return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n  }\n\n  // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n  getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n    const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n    const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n    const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n    // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n    return irisCoords.map((coord, i) => {\n      let z = averageZ;\n      if (i === 2) {\n        z = upperCenterZ;\n      } else if (i === 4) {\n        z = lowerCenterZ;\n      }\n      return [coord[0], coord[1], z];\n    });\n  }\n\n  async predict(input, config) {\n    let useFreshBox = false;\n    // run new detector every skipFrames unless we only want box to start with\n    let detector;\n    if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n      detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (!config.videoOptimized || (detector && detector.boxes && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces)))) {\n      this.storedBoxes = [];\n      this.detectedFaces = 0;\n      for (const possible of detector.boxes) {\n        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n      }\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n\n    if (config.face.detector.skipInitial && this.detectedFaces === 0) this.skipped = 0;\n\n    if (useFreshBox) {\n      if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n        this.storedBoxes = [];\n        this.detectedFaces = 0;\n        return null;\n      }\n      for (let i = 0; i < this.storedBoxes.length; i++) {\n        const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n        const enlargedBox = bounding.enlargeBox(scaledBox);\n        const squarifiedBox = bounding.squarifyBox(enlargedBox);\n        const landmarks = this.storedBoxes[i].landmarks.arraySync();\n        const confidence = this.storedBoxes[i].confidence;\n        this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n      }\n    }\n    if (detector && detector.boxes) {\n      detector.boxes.forEach((prediction) => {\n        prediction.box.startPoint.dispose();\n        prediction.box.endPoint.dispose();\n        prediction.landmarks.dispose();\n      });\n    }\n\n    let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n      const boxConfidence = box.confidence;\n\n      // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n      let face;\n      let angle = 0;\n      let rotationMatrix;\n\n      if (config.face.detector.rotation && config.face.mesh.enabled && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.boxSize, this.boxSize]).div(255);\n      } else {\n        rotationMatrix = util.IDENTITY_MATRIX;\n        const clonedImage = input.clone();\n        if (config.face.mesh.enabled) face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.meshSize, this.meshSize]).div(255);\n        else face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, clonedImage, [this.boxSize, this.boxSize]).div(255);\n      }\n\n      // if we're not going to produce mesh, don't spend time with further processing\n      if (!config.face.mesh.enabled) {\n        const prediction = {\n          coords: null,\n          box,\n          faceConfidence: null,\n          boxConfidence,\n          confidence: box.confidence,\n          image: face,\n        };\n        return prediction;\n      }\n\n      const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = confidence.dataSync()[0];\n      if (faceConfidence < config.face.detector.minConfidence) return null; // if below confidence just exit\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = coordsReshaped.arraySync();\n\n      if (config.face.iris.enabled) {\n        const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], true);\n        const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1]);\n        const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n        const eyePredictionsData = eyePredictions.dataSync();\n        const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n        const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n        const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n        const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n        const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n        if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n          // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n          // So we only update a single contour line above and below the eye.\n        } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n          replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n        } else { // User is looking towards the left.\n          replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n        }\n        const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n        const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n        rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n      }\n\n      // override box from detection with one calculated from mesh\n      const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n      box = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData), 1.5); // redefine box with mesh calculated one\n      const transformedCoords = tf.tensor2d(transformedCoordsData);\n\n      // do rotation one more time with mesh keypoints if we want to return perfect image\n      if (config.face.detector.rotation && config.face.mesh.enabled && (config.face.description.enabled || config.face.embedding.enabled) && tf.ENV.flags.IS_BROWSER) {\n        const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= meshLandmarks.count) ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;\n        angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n        const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n        const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n        const rotatedImage = tf.image.rotateWithOffset(input.toFloat(), angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node\n        rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n        face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);\n      }\n\n      const prediction = {\n        coords: transformedCoords,\n        box,\n        faceConfidence,\n        boxConfidence,\n        image: face,\n        rawCoords,\n      };\n\n      // updated stored cache values\n      const squarifiedLandmarksBox = bounding.squarifyBox(box);\n      this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoordsData, confidence: box.confidence, faceConfidence };\n\n      return prediction;\n    }));\n\n    results = results.filter((a) => a !== null);\n    // remove cache entries for detected boxes on low confidence\n    if (config.face.mesh.enabled) this.storedBoxes = this.storedBoxes.filter((a) => a.faceConfidence > config.face.detector.minConfidence);\n    this.detectedFaces = results.length;\n\n    return results;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint, landmarks };\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint };\n}\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, landmarks: box.landmarks };\n}\n", "export const IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport function radToDegrees(rad) {\n  return rad * 180 / Math.PI;\n}\n\nexport function buildTranslationMatrix(x, y) {\n  return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n\nexport function xyDistanceBetweenPoints(a, b) {\n  return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\n", "export const MESH_ANNOTATIONS = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468 = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 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0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468 = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33 = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68 = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nlet model;\n\nasync function estimateMultiple(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n    const scoresBuffer = allTensorBuffers[0];\n    const offsetsBuffer = allTensorBuffers[1];\n    const displacementsFwdBuffer = allTensorBuffers[2];\n    const displacementsBwdBuffer = allTensorBuffers[3];\n    const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config.body.nmsRadius, config.body.maxDetections, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses(poses, [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nasync function estimateSingle(input, res, config, inputSize) {\n  return new Promise(async (resolve) => {\n    const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config.body.scoreThreshold);\n    const scaled = util.scaleAndFlipPoses([pose], [input.shape[1], input.shape[2]], [inputSize, inputSize]);\n    resolve(scaled);\n  });\n}\n\nexport class PoseNet {\n  baseModel: any;\n  inputSize: number\n  constructor(mobilenet) {\n    this.baseModel = mobilenet;\n    this.inputSize = mobilenet.model.inputs[0].shape[1];\n    if (this.inputSize < 128) this.inputSize = 257;\n  }\n\n  async estimatePoses(input, config) {\n    const resized = util.resizeTo(input, [this.inputSize, this.inputSize]);\n    const res = this.baseModel.predict(resized, config);\n\n    const poses = (config.body.maxDetections < 2)\n      ? await estimateSingle(input, res, config, this.inputSize)\n      : await estimateMultiple(input, res, config, this.inputSize);\n\n    res.heatmapScores.dispose();\n    res.offsets.dispose();\n    res.displacementFwd.dispose();\n    res.displacementBwd.dispose();\n    resized.dispose();\n\n    return poses;\n  }\n\n  dispose() {\n    this.baseModel.dispose();\n  }\n}\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  const mobilenet = new modelBase.BaseModel(model);\n  const poseNet = new PoseNet(mobilenet);\n  return poseNet;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction nameOutputResultsMobileNet(results) {\n  const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n  return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nexport class BaseModel {\n  model: any;\n  constructor(model) {\n    this.model = model;\n  }\n\n  predict(input) {\n    return tf.tidy(() => {\n      const asFloat = input.toFloat().div(127.5).sub(1.0);\n      const asBatch = asFloat.expandDims(0);\n      const results = this.model.predict(asBatch);\n      const results3d = results.map((y) => y.squeeze([0]));\n      const namedResults = nameOutputResultsMobileNet(results3d);\n      return {\n        heatmapScores: namedResults.heatmap.sigmoid(),\n        offsets: namedResults.offsets,\n        displacementFwd: namedResults.displacementFwd,\n        displacementBwd: namedResults.displacementBwd,\n      };\n    });\n  }\n\n  dispose() {\n    this.model.dispose();\n  }\n}\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n  return Math.floor(k / 2);\n}\nexport class MaxHeap {\n  priorityQueue: any;\n  numberOfElements: number;\n  getElementValue: any;\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() {\n    return this.numberOfElements === -1;\n  }\n\n  size() {\n    return this.numberOfElements + 1;\n  }\n\n  all() {\n    return this.priorityQueue.slice(0, this.numberOfElements + 1);\n  }\n\n  max() {\n    return this.priorityQueue[0];\n  }\n\n  swim(k) {\n    while (k > 0 && this.less(half(k), k)) {\n      this.exchange(k, half(k));\n      k = half(k);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < scoreThreshold) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n        }\n      }\n    }\n  }\n  return queue;\n}\n", "export const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const NUM_KEYPOINTS = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport const partChannels = [\n  'left_face',\n  'right_face',\n  'right_upper_leg_front',\n  'right_lower_leg_back',\n  'right_upper_leg_back',\n  'left_lower_leg_front',\n  'left_upper_leg_front',\n  'left_upper_leg_back',\n  'left_lower_leg_back',\n  'right_feet',\n  'right_lower_leg_front',\n  'left_feet',\n  'torso_front',\n  'torso_back',\n  'right_upper_arm_front',\n  'right_upper_arm_back',\n  'right_lower_arm_back',\n  'left_lower_arm_front',\n  'left_upper_arm_front',\n  'left_upper_arm_back',\n  'left_lower_arm_back',\n  'right_hand',\n  'right_lower_arm_front',\n  'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nexport function getPointsConfidence(heatmapScores, heatMapCoords) {\n  const numKeypoints = heatMapCoords.shape[0];\n  const result = new Float32Array(numKeypoints);\n  for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n    const y = heatMapCoords.get(keypoint, 0);\n    const x = heatMapCoords.get(keypoint, 1);\n    result[keypoint] = heatmapScores.get(y, x, keypoint);\n  }\n  return result;\n}\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n  return {\n    y: offsetsBuffer.get(y, x, keypoint),\n    x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n  };\n}\n\nexport function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n  const result: Array<number> = [];\n  for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n    const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n    const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n    const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n    result.push(y);\n    result.push(x);\n  }\n  return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\n\nexport function getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n  return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\n\nfunction mod(a, b) {\n  return tf.tidy(() => {\n    const floored = a.div(tf.scalar(b, 'int32'));\n    return a.sub(floored.mul(tf.scalar(b, 'int32')));\n  });\n}\n\nexport function argmax2d(inputs) {\n  const [height, width, depth] = inputs.shape;\n  return tf.tidy(() => {\n    const reshaped = inputs.reshape([height * width, depth]);\n    const coords = reshaped.argMax(0);\n    const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n    const xCoords = mod(coords, width).expandDims(1);\n    return tf.concat([yCoords, xCoords], 1);\n  });\n}\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\n\nconst defaultOutputStride = 16;\n\nfunction getDisplacement(edgeId, point, displacements) {\n  const numEdges = displacements.shape[2] / 2;\n  return {\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, numEdges + edgeId),\n  };\n}\n\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n  return {\n    y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n  const [height, width] = scoresBuffer.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n  const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n  const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n    const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n    targetKeypoint = vectors.addVectors({\n      x: targetKeypointIndices.x * outputStride,\n      y: targetKeypointIndices.y * outputStride,\n    }, { x: offsetPoint.x, y: offsetPoint.y });\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n  const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n  return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nexport function decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n  const numParts = scores.shape[2];\n  const numEdges = parentToChildEdges.length;\n  const instanceKeypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const { part: rootPart, score: rootScore } = root;\n  const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n  instanceKeypoints[rootPart.id] = {\n    score: rootScore,\n    part: keypoints.partNames[rootPart.id],\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceKeypointId = parentToChildEdges[edge];\n    const targetKeypointId = childToParentEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceKeypointId = childToParentEdges[edge];\n    const targetKeypointId = parentToChildEdges[edge];\n    if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n      instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n    }\n  }\n  return instanceKeypoints;\n}\n\nexport async function decodeSinglePose(heatmapScores, offsets, minScore) {\n  let totalScore = 0.0;\n  const heatmapValues = decoders.argmax2d(heatmapScores);\n  const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n  const scoresBuffer = allTensorBuffers[0];\n  const offsetsBuffer = allTensorBuffers[1];\n  const heatmapValuesBuffer = allTensorBuffers[2];\n  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);\n  const offsetPointsBuffer = await offsetPoints.buffer();\n  const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n  const instanceKeypoints = keypointConfidence.map((score, i) => {\n    totalScore += score;\n    return {\n      position: {\n        y: offsetPointsBuffer.get(i, 0),\n        x: offsetPointsBuffer.get(i, 1),\n      },\n      part: keypoints.partNames[i],\n      score,\n    };\n  });\n  const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > minScore);\n  heatmapValues.dispose();\n  offsetPoints.dispose();\n  return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\nconst defaultOutputStride = 16;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId].position;\n    return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n  const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nexport function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, nmsRadius, maxDetections, scoreThreshold) {\n  const poses: Array<{ keypoints: any, score: number }> = [];\n  const queue = buildParts.buildPartWithScoreQueue(scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n  const squaredNmsRadius = nmsRadius ^ 2;\n  // Generate at most maxDetections object instances per image in decreasing root part score order.\n  while (poses.length < maxDetections && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);\n    if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n    const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n    if (score > scoreThreshold) poses.push({ keypoints, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n", "import * as kpt from './keypoints';\n\nexport function eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence) {\n  return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nexport function getBoundingBox(keypoints) {\n  return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: NEGATIVE_INFINITY,\n    maxY: NEGATIVE_INFINITY,\n    minX: POSITIVE_INFINITY,\n    minY: POSITIVE_INFINITY,\n  });\n}\n\nexport function getBoundingBoxPoints(keypoints) {\n  const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n  return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\n\nexport async function toTensorBuffers3D(tensors) {\n  return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\n\nexport function scalePose(pose, scaleY, scaleX) {\n  return {\n    score: pose.score,\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score,\n      part,\n      position: { x: Math.trunc(position.x * scaleX), y: Math.trunc(position.y * scaleY) },\n    })),\n  };\n}\n\nexport function resizeTo(image, [targetH, targetW]) {\n  const input = image.squeeze(0);\n  const resized = input.resizeBilinear([targetH, targetW]);\n  input.dispose();\n  return resized;\n}\n\nexport function scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n  const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n  return scaledPoses;\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n  thumb: [1, 2, 3, 4],\n  indexFinger: [5, 6, 7, 8],\n  middleFinger: [9, 10, 11, 12],\n  ringFinger: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palmBase: [0],\n};\n\nexport class HandPose {\n  handPipeline: any;\n\n  constructor(handPipeline) {\n    this.handPipeline = handPipeline;\n  }\n\n  static getAnnotations() {\n    return MESH_ANNOTATIONS;\n  }\n\n  async estimateHands(input, config) {\n    const predictions = await this.handPipeline.estimateHands(input, config);\n    if (!predictions) return [];\n    const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];\n    for (const prediction of predictions) {\n      const annotations = {};\n      if (prediction.landmarks) {\n        for (const key of Object.keys(MESH_ANNOTATIONS)) {\n          annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n        }\n      }\n      const box = prediction.box ? [\n        Math.max(0, prediction.box.topLeft[0]),\n        Math.max(0, prediction.box.topLeft[1]),\n        Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),\n        Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),\n      ] : [];\n      const boxRaw = [\n        (prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.topLeft[1]) / input.shape[1],\n        (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],\n        (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],\n      ];\n      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });\n    }\n    return hands;\n  }\n}\n\nlet handDetectorModel;\nlet handPoseModel;\nexport async function load(config): Promise<HandPose> {\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n      config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n    ]);\n    if (config.hand.enabled) {\n      if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);\n      else if (config.debug) log('load model:', handDetectorModel.modelUrl);\n      if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);\n      else if (config.debug) log('load model:', handPoseModel.modelUrl);\n    }\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel.modelUrl);\n    if (config.debug) log('cached model:', handPoseModel.modelUrl);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);\n  const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);\n  const handPose = new HandPose(handPipeline);\n  return handPose;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nexport class HandDetector {\n  model: any;\n  anchors: any;\n  anchorsTensor: any;\n  inputSize: number;\n  inputSizeTensor: any;\n  doubleInputSizeTensor: any;\n\n  constructor(model, inputSize, anchorsAnnotated) {\n    this.model = model;\n    this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = inputSize;\n    this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    return tf.tidy(() => {\n      const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n      const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n      const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n      const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n      const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n      return tf.concat2d([startPoints, endPoints], 1);\n    });\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    return tf.tidy(() => {\n      const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n      return tf.mul(landmarks, this.inputSizeTensor);\n    });\n  }\n\n  async getBoxes(input, config) {\n    const batched = this.model.predict(input);\n    const predictions = batched.squeeze();\n    batched.dispose();\n    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n    const scores = scoresT.dataSync();\n    const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n    const boxes = this.normalizeBoxes(rawBoxes);\n    rawBoxes.dispose();\n    const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n    const filtered = filteredT.arraySync();\n\n    scoresT.dispose();\n    filteredT.dispose();\n    const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];\n    for (const index of filtered) {\n      if (scores[index] >= config.hand.minConfidence) {\n        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n        rawPalmLandmarks.dispose();\n        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n      }\n    }\n    predictions.dispose();\n    boxes.dispose();\n    return hands;\n  }\n\n  async estimateHandBounds(input, config) {\n    const inputHeight = input.shape[1];\n    const inputWidth = input.shape[2];\n    const image = tf.tidy(() => input.resizeBilinear([this.inputSize, this.inputSize]).div(127.5).sub(1));\n    const predictions = await this.getBoxes(image, config);\n    image.dispose();\n    const hands: Array<{}> = [];\n    if (!predictions || predictions.length === 0) return hands;\n    for (const prediction of predictions) {\n      const boxes = prediction.box.dataSync();\n      const startPoint = boxes.slice(0, 2);\n      const endPoint = boxes.slice(2, 4);\n      const palmLandmarks = prediction.palmLandmarks.arraySync();\n      prediction.box.dispose();\n      prediction.palmLandmarks.dispose();\n      hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));\n    }\n    return hands;\n  }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nexport class HandPipeline {\n  handDetector: any;\n  landmarkDetector: any;\n  inputSize: number;\n  storedBoxes: any;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, landmarkDetector, inputSize) {\n    this.handDetector = handDetector;\n    this.landmarkDetector = landmarkDetector;\n    this.inputSize = inputSize;\n    this.storedBoxes = [];\n    this.skipped = 0;\n    this.detectedHands = 0;\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n    return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n    const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = box.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...box.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      coord[0] + originalBoxCenter[0],\n      coord[1] + originalBoxCenter[1],\n      coord[2],\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames unless we only want box to start with\n    let boxes;\n    if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n      boxes = await this.handDetector.estimateHandBounds(image, config);\n      this.skipped = 0;\n    }\n    if (config.videoOptimized) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{}> = [];\n\n    if (config.hand.skipInitial && this.detectedHands === 0) this.skipped = 0;\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n        const palmCenter = box.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = croppedInput.div(255);\n        croppedInput.dispose();\n        rotatedImage.dispose();\n        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n        handImage.dispose();\n        const confidence = confidenceT.dataSync()[0];\n        confidenceT.dispose();\n        if (confidence >= config.hand.minConfidence) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = keypointsReshaped.arraySync();\n          keypoints.dispose();\n          keypointsReshaped.dispose();\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = nextBoundingBox;\n          const result = {\n            landmarks: coords,\n            confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        keypoints.dispose();\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n        const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n        const result = {\n          confidence: currentBox.confidence,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    return hands;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n}\n", "export function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "export const anchors = [\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.015625,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.390625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.421875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.453125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.484375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.515625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.546875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.578125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.609375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.640625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.671875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.703125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.734375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.765625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.796875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.828125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.859375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.890625,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.921875,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.953125,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.984375,\n    y_center: 0.046875,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.015625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.046875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.078125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.109375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.140625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.171875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.203125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.234375,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.265625,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.296875,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.328125,\n    y_center: 0.078125,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.359375,\n    y_center: 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y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.1875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.3125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.4375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.5625,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.6875,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.8125,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n  {\n    w: 1,\n    h: 1,\n    x_center: 0.9375,\n    y_center: 0.9375,\n  },\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport * as annotations from './annotations';\n\nlet model;\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);\n    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if (!config.body.enabled) return null;\n  const imgSize = { width: image.shape[2], height: image.shape[1] };\n  const resize = tf.image.resizeBilinear(image, [model.width, model.height], false);\n  const normalize = tf.div(resize, [255.0]);\n  resize.dispose();\n  let points;\n  if (!config.profile) { // run through profiler or just execute\n    const resT = await model.predict(normalize);\n    // const segmentationT = resT.find((t) => (t.size === 16384))?.squeeze();\n    // const segmentation = segmentationT.arraySync(); // array 128 x 128\n    // segmentationT.dispose();\n    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync(); // order of output tensors may change between models, full has 195 and upper has 155 items\n    resT.forEach((t) => t.dispose());\n  } else {\n    const profileData = await tf.profile(() => model.predict(normalize));\n    points = profileData.result.find((t) => (t.size === 195 || t.size === 155)).dataSync();\n    profileData.result.forEach((t) => t.dispose());\n    profile.run('blazepose', profileData);\n  }\n  normalize.dispose();\n  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];\n  const labels = points.length === 195 ? annotations.full : annotations.upper; // full model has 39 keypoints, upper has 31 keypoints\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    keypoints.push({\n      id: i,\n      part: labels[i],\n      position: {\n        x: Math.trunc(imgSize.width * points[depth * i + 0] / 255), // return normalized x value istead of 0..255\n        y: Math.trunc(imgSize.height * points[depth * i + 1] / 255), // return normalized y value istead of 0..255\n        z: Math.trunc(points[depth * i + 2]) + 0, // fix negative zero\n      },\n      score: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100, // reverse sigmoid value\n      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100, // reverse sigmoid value\n    });\n  }\n  const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  return [{ score, keypoints }];\n}\n", "export const full = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftPalm',\n  'rightPalm',\n  'leftIndex',\n  'rightIndex',\n  'leftPinky',\n  'rightPinky',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n  'leftHeel',\n  'rightHeel',\n  'leftFoot',\n  'rightFoot',\n  'midHip',\n  'forehead',\n  'leftThumb',\n  'leftHand',\n  'rightThumb',\n  'rightHand',\n];\n\nexport const upper = [\n  'nose',\n  'leftEyeInside',\n  'leftEye',\n  'leftEyeOutside',\n  'rightEyeInside',\n  'rightEye',\n  'rightEyeOutside',\n  'leftEar',\n  'rightEar',\n  'leftMouth',\n  'rightMouth',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'left:15',\n  'right:16',\n  'left:17',\n  'right:18',\n  'left:19',\n  'right:20',\n  'left:21',\n  'right:22',\n  'leftChest',\n  'rightChest',\n  'neck',\n  'forehead',\n  'left:27',\n  'right:28',\n  'left:29',\n  'right:30',\n];\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\n\nlet model;\nlet keypoints: Array<any> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));\n    if (!model || !model.modelUrl) log('load model failed:', config.body.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nfunction max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  return tf.tidy(() => {\n    // modulus op implemented in tf\n    const mod = (a, b) => tf.sub(a, tf.mul(tf.div(a, tf.scalar(b, 'int32')), tf.scalar(b, 'int32')));\n    // combine all data\n    const reshaped = tf.reshape(inputs, [height * width]);\n    // get highest score\n    const score = tf.max(reshaped, 0).dataSync()[0];\n    if (score > minScore) {\n      // skip coordinate calculation is score is too low\n      const coords = tf.argMax(reshaped, 0);\n      const x = mod(coords, width).dataSync()[0];\n      const y = tf.div(coords, tf.scalar(width, 'int32')).dataSync()[0];\n      return [x, y, score];\n    }\n    return [0, 0, score];\n  });\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  if ((skipped < config.body.skipFrames) && config.videoOptimized && Object.keys(keypoints).length > 0) {\n    skipped++;\n    return keypoints;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, 2);\n      const norm = enhance.sub(1);\n      return norm;\n    });\n\n    let resT;\n\n    if (!config.profile) {\n      if (config.body.enabled) resT = await model.predict(tensor);\n    } else {\n      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};\n      resT = profileT.result.clone();\n      profileT.result.dispose();\n      profile.run('body', profileT);\n    }\n    tensor.dispose();\n\n    if (resT) {\n      const parts: Array<{ id, score, part, position: { x, y }, positionRaw: { xRaw, yRaw} }> = [];\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, score] = max2d(stack[id], config.body.scoreThreshold);\n        if (score > config.body.scoreThreshold) {\n          parts.push({\n            id,\n            score: Math.round(100 * score) / 100,\n            part: bodyParts[id],\n            positionRaw: {\n              xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1\n              yRaw: y / model.inputs[0].shape[1], // y normalized to 0..1\n            },\n            position: {\n              x: Math.round(image.shape[2] * x / model.inputs[0].shape[2]), // x normalized to input image size\n              y: Math.round(image.shape[1] * y / model.inputs[0].shape[1]), // y normalized to input image size\n            },\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n      keypoints = parts;\n    }\n    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    resolve([{ score, keypoints }]);\n  });\n}\n", "import { log, join } from '../helpers';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile';\nimport { labels } from './labels';\n\nlet model;\nlet last: Array<{}> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config) {\n  if (!model) {\n    model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));\n    const inputs = Object.values(model.modelSignature['inputs']);\n    model.inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : null;\n    if (!model.inputSize) throw new Error(`Human: Cannot determine model inputSize: ${config.object.modelPath}`);\n    if (!model || !model.modelUrl) log('load model failed:', config.object.modelPath);\n    else if (config.debug) log('load model:', model.modelUrl);\n  } else if (config.debug) log('cached model:', model.modelUrl);\n  return model;\n}\n\nasync function process(res, inputSize, outputShape, config) {\n  let id = 0;\n  let results: Array<{ score: number, strideSize: number, class: number, label: string, center: number[], centerRaw: number[], box: number[], boxRaw: number[] }> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();\n      const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself\n      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > config.object.minConfidence && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label,\n              center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)),\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => a.boxRaw);\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: any[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxResults, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = nms.dataSync();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((a, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image, config) {\n  if (!model) return null;\n  // console.log(skipped, config.object.skipFrames, config.videoOptimized, ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)));\n  if ((skipped < config.object.skipFrames) && config.videoOptimized && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  if (config.videoOptimized) skipped = 0;\n  else skipped = Number.MAX_SAFE_INTEGER;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2], image.shape[1]];\n    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);\n    const norm = resize.div(255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    norm.dispose();\n    resize.dispose();\n\n    let objectT;\n    if (!config.profile) {\n      if (config.object.enabled) objectT = await model.predict(transpose);\n    } else {\n      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};\n      objectT = profileObject.result;\n      profile.run('object', profileObject);\n    }\n    transpose.dispose();\n\n    const obj = await process(objectT, model.inputSize, outputSize, config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "export const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "export const body = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n  }\n  return gestures;\n};\n\nexport const face = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 0) {\n      const eyeFacing = res[i].mesh[33][2] - res[i].mesh[263][2];\n      if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n      else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2];\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n  }\n  return gestures;\n};\n\nexport const hand = (res) => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: string }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: number }> = [];\n    for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n      if (finger !== 'palmBase' && Array.isArray(pos)) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n    }\n  }\n  return gestures;\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas;\nlet outCanvas;\n// instance of fximage\nlet fx;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport function process(input, config): { tensor: typeof tf.Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement } {\n  let tensor;\n  if (!input) throw new Error('Human: Input is missing');\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('Human: Input type is not recognized');\n  }\n  if (input instanceof tf.Tensor) {\n    if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3) tensor = tf.clone(input);\n    else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);\n  } else {\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = targetWidth * originalHeight / originalWidth;\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = targetHeight * originalWidth / originalHeight;\n    }\n    if (config.filter.width > 0) targetWidth = config.filter.width;\n    else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n    if (config.filter.height > 0) targetHeight = config.filter.height;\n    else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('Human: Input cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) {\n      inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      if (inCanvas?.width !== targetWidth) inCanvas.width = targetWidth;\n      if (inCanvas?.height !== targetHeight) inCanvas.height = targetHeight;\n    }\n    const ctx = inCanvas.getContext('2d');\n    if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n    else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n    if (config.filter.enabled) {\n      if (!fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) {\n        outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas?.width, inCanvas?.height) : document.createElement('canvas');\n        if (outCanvas?.width !== inCanvas?.width) outCanvas.width = inCanvas?.width;\n        if (outCanvas?.height !== inCanvas?.height) outCanvas.height = inCanvas?.height;\n        // log('created FX filter');\n        fx = tf.ENV.flags.IS_BROWSER ? new fxImage.GLImageFilter({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n      }\n      if (!fx) return { tensor: null, canvas: inCanvas };\n      fx.reset();\n      fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n      if (config.filter.contrast !== 0) fx.addFilter('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.addFilter('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.addFilter('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.addFilter('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.addFilter('hue', config.filter.hue);\n      if (config.filter.negative) fx.addFilter('negative');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.vintage) fx.addFilter('brownie');\n      if (config.filter.sepia) fx.addFilter('sepia');\n      if (config.filter.kodachrome) fx.addFilter('kodachrome');\n      if (config.filter.technicolor) fx.addFilter('technicolor');\n      if (config.filter.polaroid) fx.addFilter('polaroid');\n      if (config.filter.pixelate !== 0) fx.addFilter('pixelate', config.filter.pixelate);\n      fx.apply(inCanvas);\n      // read pixel data\n      /*\n      const gl = outCanvas.getContext('webgl');\n      if (gl) {\n        const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n        const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n        gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n        // gl returns rbga while we only need rgb, so discarding alpha channel\n        // gl returns starting point as lower left, so need to invert vertical\n        let i = 0;\n        for (let y = outCanvas.height - 1; y >= 0; y--) {\n          for (let x = 0; x < outCanvas.width; x++) {\n            const index = (x + y * outCanvas.width) * 4;\n            pixBuffer[i++] = glBuffer[index + 0];\n            pixBuffer[i++] = glBuffer[index + 1];\n            pixBuffer[i++] = glBuffer[index + 2];\n          }\n        }\n        outCanvas.data = pixBuffer;\n      }\n      */\n    } else {\n      outCanvas = inCanvas;\n      if (fx) fx = null;\n    }\n    let pixels;\n    if (outCanvas.data) { // if we have data, just convert to tensor\n      const shape = [outCanvas.height, outCanvas.width, 3];\n      pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n    } else if (outCanvas instanceof ImageData) { // if input is imagedata, just use it\n      pixels = tf.browser.fromPixels(outCanvas);\n    } else if (config.backend === 'webgl' || config.backend === 'humangl') { // tf kernel-optimized method to get imagedata\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      pixels = tf.browser.fromPixels(tempCanvas);\n    } else { // cpu and wasm kernel does not implement efficient fromPixels method\n      // we can use canvas as-is as it already has a context, so we do a silly one more canvas\n      const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n      tempCanvas.width = targetWidth;\n      tempCanvas.height = targetHeight;\n      const tempCtx = tempCanvas.getContext('2d');\n      tempCtx?.drawImage(outCanvas, 0, 0);\n      const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n      pixels = tf.browser.fromPixels(data);\n    }\n    const casted = pixels.toFloat();\n    tensor = casted.expandDims(0);\n    pixels.dispose();\n    casted.dispose();\n  }\n  const canvas = config.filter.return ? outCanvas : null;\n  return { tensor, canvas };\n}\n", "/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n<https://github.com/phoboslab/WebGLImageFilter>\n*/\n\nfunction GLProgram(gl, vertexSource, fragmentSource) {\n  const _collect = function (source, prefix, collection) {\n    const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n    source.replace(r, (match, name) => {\n      collection[name] = 0;\n      return match;\n    });\n  };\n\n  const _compile = function (source, type) {\n    const shader = gl.createShader(type);\n    gl.shaderSource(shader, source);\n    gl.compileShader(shader);\n    if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n    return shader;\n  };\n\n  this.uniform = {};\n  this.attribute = {};\n  const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n  const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n  this.id = gl.createProgram();\n  gl.attachShader(this.id, _vsh);\n  gl.attachShader(this.id, _fsh);\n  gl.linkProgram(this.id);\n\n  if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n\n  gl.useProgram(this.id);\n  // Collect attributes\n  _collect(vertexSource, 'attribute', this.attribute);\n  for (const a in this.attribute) this.attribute[a] = gl.getAttribLocation(this.id, a);\n  // Collect uniforms\n  _collect(vertexSource, 'uniform', this.uniform);\n  _collect(fragmentSource, 'uniform', this.uniform);\n  for (const u in this.uniform) this.uniform[u] = gl.getUniformLocation(this.id, u);\n}\n\n// export const GLImageFilter = function (params) {\nexport function GLImageFilter(params) {\n  if (!params) params = { };\n  let _drawCount = 0;\n  let _sourceTexture = null;\n  let _lastInChain = false;\n  let _currentFramebufferIndex = -1;\n  let _tempFramebuffers = [null, null];\n  let _filterChain = [];\n  let _width = -1;\n  let _height = -1;\n  let _vertexBuffer = null;\n  let _currentProgram = null;\n  const _filter = {};\n  const _canvas = params.canvas || document.createElement('canvas');\n  // key is the shader program source, value is the compiled program\n  const _shaderProgramCache = { };\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = _canvas.getContext('webgl');\n  if (!gl) throw new Error('Filter: getContext() failed');\n\n  this.addFilter = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const filter = _filter[name];\n    _filterChain.push({ func: filter, args });\n  };\n\n  this.reset = function () {\n    _filterChain = [];\n  };\n\n  const _resize = function (width, height) {\n    // Same width/height? Nothing to do here\n    if (width === _width && height === _height) { return; }\n    _canvas.width = width;\n    _width = width;\n    _canvas.height = height;\n    _height = height;\n    // Create the context if we don't have it yet\n    if (!_vertexBuffer) {\n      // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      const vertices = new Float32Array([\n        -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n        -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n      ]);\n      // eslint-disable-next-line no-unused-expressions\n      (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, _width, _height);\n    // Delete old temp framebuffers\n    _tempFramebuffers = [null, null];\n  };\n\n  const _createFramebufferTexture = function (width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  };\n\n  const _getTempFramebuffer = function (index) {\n    _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n    return _tempFramebuffers[index];\n  };\n\n  const _draw = function (flags = null) {\n    let source = null;\n    let target = null;\n    let flipY = false;\n    // Set up the source\n    if (_drawCount === 0) {\n      // First draw call - use the source texture\n      source = _sourceTexture;\n    } else {\n      // All following draw calls use the temp buffer last drawn to\n      source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n    }\n    _drawCount++;\n    // Set up the target\n    if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n      // Last filter in our chain - draw directly to the WebGL Canvas. We may\n      // also have to flip the image vertically now\n      target = null;\n      flipY = _drawCount % 2 === 0;\n    } else {\n      // Intermediate draw call - get a temp buffer to draw to\n      _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n      target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n    }\n    // Bind the source and target and draw the two triangles\n    gl.bindTexture(gl.TEXTURE_2D, source);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  };\n\n  this.apply = function (image) {\n    _resize(image.width, image.height);\n    _drawCount = 0;\n    // Create the texture for the input image if we haven't yet\n    if (!_sourceTexture) _sourceTexture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    // No filters? Just draw\n    if (_filterChain.length === 0) {\n      // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n      _draw();\n      return _canvas;\n    }\n    for (let i = 0; i < _filterChain.length; i++) {\n      _lastInChain = (i === _filterChain.length - 1);\n      const f = _filterChain[i];\n      f.func.apply(this, f.args || []);\n    }\n    return _canvas;\n  };\n\n  const _compileShader = function (fragmentSource) {\n    if (_shaderProgramCache[fragmentSource]) {\n      _currentProgram = _shaderProgramCache[fragmentSource];\n      gl.useProgram(_currentProgram.id);\n      return _currentProgram;\n    }\n    // Compile shaders\n    const SHADER = {};\n    SHADER.VERTEX_IDENTITY = [\n      'precision highp float;',\n      'attribute vec2 pos;',\n      'attribute vec2 uv;',\n      'varying vec2 vUv;',\n      'uniform float flipY;',\n      'void main(void) {',\n      'vUv = uv;',\n      'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n      '}',\n    ].join('\\n');\n    SHADER.FRAGMENT_IDENTITY = [\n      'precision highp float;',\n      'varying vec2 vUv;',\n      'uniform sampler2D texture;',\n      'void main(void) {',\n      'gl_FragColor = texture2D(texture, vUv);',\n      '}',\n    ].join('\\n');\n    _currentProgram = new GLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n    gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n    gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    _shaderProgramCache[fragmentSource] = _currentProgram;\n    return _currentProgram;\n  };\n\n  // -------------------------------------------------------------------------\n  // Color Matrix Filter\n  _filter.colorMatrix = function (matrix) {\n    // Create a Float32 Array and normalize the offset component to 0-1\n    const m = new Float32Array(matrix);\n    m[4] /= 255;\n    m[9] /= 255;\n    m[14] /= 255;\n    m[19] /= 255;\n    // Can we ignore the alpha value? Makes things a bit faster.\n    const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n      ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n      : _filter.colorMatrix.SHADER.WITH_ALPHA;\n    const program = _compileShader(shader);\n    gl.uniform1fv(program.uniform.m, m);\n    _draw();\n  };\n  _filter.colorMatrix.SHADER = {};\n  _filter.colorMatrix.SHADER.WITH_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n    'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n    '}',\n  ].join('\\n');\n  _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform float m[20];',\n    'void main(void) {',\n    'vec4 c = texture2D(texture, vUv);',\n    'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n    'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n    'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n    'gl_FragColor.a = c.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.brightness = function (brightness) {\n    const b = (brightness || 0) + 1;\n    _filter.colorMatrix([\n      b, 0, 0, 0, 0,\n      0, b, 0, 0, 0,\n      0, 0, b, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.saturation = function (amount) {\n    const x = (amount || 0) * 2 / 3 + 1;\n    const y = ((x - 1) * -0.5);\n    _filter.colorMatrix([\n      x, y, y, 0, 0,\n      y, x, y, 0, 0,\n      y, y, x, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturate = function () {\n    _filter.saturation(-1);\n  };\n\n  _filter.contrast = function (amount) {\n    const v = (amount || 0) + 1;\n    const o = -128 * (v - 1);\n\n    _filter.colorMatrix([\n      v, 0, 0, 0, o,\n      0, v, 0, 0, o,\n      0, 0, v, 0, o,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.negative = function () {\n    _filter.contrast(-2);\n  };\n\n  _filter.hue = function (rotation) {\n    rotation = (rotation || 0) / 180 * Math.PI;\n    const cos = Math.cos(rotation);\n    const sin = Math.sin(rotation);\n    const lumR = 0.213;\n    const lumG = 0.715;\n    const lumB = 0.072;\n\n    _filter.colorMatrix([\n      lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n      lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n      lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.desaturateLuminance = function () {\n    _filter.colorMatrix([\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.sepia = function () {\n    _filter.colorMatrix([\n      0.393, 0.7689999, 0.18899999, 0, 0,\n      0.349, 0.6859999, 0.16799999, 0, 0,\n      0.272, 0.5339999, 0.13099999, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.brownie = function () {\n    _filter.colorMatrix([\n      0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n      -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n      0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.vintagePinhole = function () {\n    _filter.colorMatrix([\n      0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n      0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n      0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.kodachrome = function () {\n    _filter.colorMatrix([\n      1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n      -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n      -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.technicolor = function () {\n    _filter.colorMatrix([\n      1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n      -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n      -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.polaroid = function () {\n    _filter.colorMatrix([\n      1.438, -0.062, -0.062, 0, 0,\n      -0.122, 1.378, -0.122, 0, 0,\n      -0.016, -0.016, 1.483, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  _filter.shiftToBGR = function () {\n    _filter.colorMatrix([\n      0, 0, 1, 0, 0,\n      0, 1, 0, 0, 0,\n      1, 0, 0, 0, 0,\n      0, 0, 0, 1, 0,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Convolution Filter\n  _filter.convolution = function (matrix) {\n    const m = new Float32Array(matrix);\n    const pixelSizeX = 1 / _width;\n    const pixelSizeY = 1 / _height;\n    const program = _compileShader(_filter.convolution.SHADER);\n    gl.uniform1fv(program.uniform.m, m);\n    gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n    _draw();\n  };\n\n  _filter.convolution.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'uniform float m[9];',\n    'void main(void) {',\n    'vec4 c11 = texture2D(texture, vUv - px);', // top left\n    'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n    'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n    'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n    'vec4 c22 = texture2D(texture, vUv);', // mid center\n    'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n    'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n    'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n    'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n    'gl_FragColor = ',\n    'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n    'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n    'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n    'gl_FragColor.a = c22.a;',\n    '}',\n  ].join('\\n');\n\n  _filter.detectEdges = function () {\n    _filter.convolution.call(this, [\n      0, 1, 0,\n      1, -4, 1,\n      0, 1, 0,\n    ]);\n  };\n\n  _filter.sobelX = function () {\n    _filter.convolution.call(this, [\n      -1, 0, 1,\n      -2, 0, 2,\n      -1, 0, 1,\n    ]);\n  };\n\n  _filter.sobelY = function () {\n    _filter.convolution.call(this, [\n      -1, -2, -1,\n      0, 0, 0,\n      1, 2, 1,\n    ]);\n  };\n\n  _filter.sharpen = function (amount) {\n    const a = amount || 1;\n    _filter.convolution.call(this, [\n      0, -1 * a, 0,\n      -1 * a, 1 + 4 * a, -1 * a,\n      0, -1 * a, 0,\n    ]);\n  };\n\n  _filter.emboss = function (size) {\n    const s = size || 1;\n    _filter.convolution.call(this, [\n      -2 * s, -1 * s, 0,\n      -1 * s, 1, 1 * s,\n      0, 1 * s, 2 * s,\n    ]);\n  };\n\n  // -------------------------------------------------------------------------\n  // Blur Filter\n  _filter.blur = function (size) {\n    const blurSizeX = (size / 7) / _width;\n    const blurSizeY = (size / 7) / _height;\n    const program = _compileShader(_filter.blur.SHADER);\n    // Vertical\n    gl.uniform2f(program.uniform.px, 0, blurSizeY);\n    _draw(DRAW.INTERMEDIATE);\n    // Horizontal\n    gl.uniform2f(program.uniform.px, blurSizeX, 0);\n    _draw();\n  };\n\n  _filter.blur.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform sampler2D texture;',\n    'uniform vec2 px;',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;',\n    'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;',\n    '}',\n  ].join('\\n');\n\n  // -------------------------------------------------------------------------\n  // Pixelate Filter\n  _filter.pixelate = function (size) {\n    const blurSizeX = (size) / _width;\n    const blurSizeY = (size) / _height;\n    const program = _compileShader(_filter.pixelate.SHADER);\n    // Horizontal\n    gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n    _draw();\n  };\n\n  _filter.pixelate.SHADER = [\n    'precision highp float;',\n    'varying vec2 vUv;',\n    'uniform vec2 size;',\n    'uniform sampler2D texture;',\n    'vec2 pixelate(vec2 coord, vec2 size) {',\n    'return floor( coord / size ) * size;',\n    '}',\n    'void main(void) {',\n    'gl_FragColor = vec4(0.0);',\n    'vec2 coord = pixelate(vUv, size);',\n    'gl_FragColor += texture2D(texture, coord);',\n    '}',\n  ].join('\\n');\n}\n", "import { defaults } from '../config';\nimport { TRI468 as triangulation } from '../blazeface/coords';\nimport { mergeDeep } from '../helpers';\n\n/**\n * Draw Options\n * Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n * -color: draw color\n * -labelColor: color for labels\n * -shadowColor: optional shadow color for labels\n * -font: font for labels\n * -lineHeight: line height for labels, used for multi-line labels,\n * -lineWidth: width of any lines,\n * -pointSize: size of any point,\n * -roundRect: for boxes, round corners by this many pixels,\n * -drawPoints: should points be drawn,\n * -drawLabels: should labels be drawn,\n * -drawBoxes: should boxes be drawn,\n * -drawPolygons: should polygons be drawn,\n * -fillPolygons: should drawn polygons be filled,\n * -useDepth: use z-axis coordinate as color shade,\n * -useCurves: draw polygons as cures or as lines,\n * -bufferedOutput: experimental: allows to call draw methods multiple times for each detection and interpolate results between results thus achieving smoother animations\n * -useRawBoxes: Boolean: internal: use non-normalized coordinates when performing draw methods,\n */\nexport interface DrawOptions {\n  color: string,\n  labelColor: string,\n  shadowColor: string,\n  font: string,\n  lineHeight: number,\n  lineWidth: number,\n  pointSize: number,\n  roundRect: number,\n  drawPoints: Boolean,\n  drawLabels: Boolean,\n  drawBoxes: Boolean,\n  drawPolygons: Boolean,\n  fillPolygons: Boolean,\n  useDepth: Boolean,\n  useCurves: Boolean,\n  bufferedOutput: Boolean,\n  useRawBoxes: Boolean,\n}\n\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>20,\n  lineWidth: <number>6,\n  pointSize: <number>2,\n  roundRect: <number>28,\n  drawPoints: <Boolean>false,\n  drawLabels: <Boolean>true,\n  drawBoxes: <Boolean>true,\n  drawPolygons: <Boolean>true,\n  fillPolygons: <Boolean>false,\n  useDepth: <Boolean>true,\n  useCurves: <Boolean>false,\n  bufferedOutput: <Boolean>false,\n  useRawBoxes: <Boolean>false,\n};\n\nfunction point(ctx, x, y, z = 0, localOptions) {\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx, x, y, width, height, localOptions) {\n  ctx.beginPath();\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && pt[2] ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], parseInt(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx, points: number[] = [], localOptions) {\n  if (points === undefined || points.length === 0) return;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.font = localOptions.font;\n  ctx.fillStyle = localOptions.color;\n  let i = 1;\n  for (let j = 0; j < result.length; j++) {\n    let where:any[] = [];\n    let what:any[] = [];\n    [where, what] = Object.entries(result[j]);\n    if ((what.length > 1) && (what[1].length > 0)) {\n      const person = where[1] > 0 ? `#${where[1]}` : '';\n      const label = `${where[0]} ${person}: ${what[1]}`;\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n      i += 1;\n    }\n  }\n}\n\nexport async function face(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) {\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * f.boxRaw[0], inCanvas.height * f.boxRaw[1], inCanvas.width * f.boxRaw[2], inCanvas.height * f.boxRaw[3], localOptions);\n      else rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    }\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face confidence: ${Math.trunc(100 * f.confidence)}%`);\n    if (f.genderConfidence) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderConfidence)}% confident`);\n    // if (f.genderConfidence) labels.push(f.gender);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`iris distance: ${f.iris}`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.angle.roll) labels.push(`roll: ${Math.trunc(100 * f.rotation.angle.roll) / 100} yaw:${Math.trunc(100 * f.rotation.angle.yaw) / 100} pitch:${Math.trunc(100 * f.rotation.angle.pitch) / 100}`);\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = localOptions.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * localOptions.lineHeight + f.box[1];\n      if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n        ctx.fillStyle = localOptions.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = localOptions.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n    ctx.lineWidth = 1;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n        // for (const pt of f.meshRaw) point(ctx, pt[0] * inCanvas.offsetWidth, pt[1] * inCanvas.offsetHeight, pt[2]);\n      }\n      if (localOptions.drawPolygons) {\n        ctx.lineWidth = 1;\n        for (let i = 0; i < triangulation.length / 3; i++) {\n          const points = [\n            triangulation[i * 3 + 0],\n            triangulation[i * 3 + 1],\n            triangulation[i * 3 + 2],\n          ].map((index) => f.mesh[index]);\n          lines(ctx, points, localOptions);\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations.leftEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations.rightEyeIris) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n          const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n          ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n      }\n    }\n  }\n}\n\nconst lastDrawnPose:any[] = [];\nexport async function body(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);\n    if (!lastDrawnPose[i] && localOptions.bufferedOutput) lastDrawnPose[i] = { ...result[i] };\n    ctx.strokeStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    if (localOptions.drawPoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : localOptions.color;\n        if (localOptions.bufferedOutput) {\n          lastDrawnPose[i].keypoints[pt][0] = (lastDrawnPose[i].keypoints[pt][0] + result[i].keypoints[pt].position.x) / 2;\n          lastDrawnPose[i].keypoints[pt][1] = (lastDrawnPose[i].keypoints[pt][1] + result[i].keypoints[pt].position.y) / 2;\n          point(ctx, lastDrawnPose[i].keypoints[pt][0], lastDrawnPose[i].keypoints[pt][1], 0, localOptions);\n        } else {\n          point(ctx, result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels) {\n      ctx.font = localOptions.font;\n      if (result[i].keypoints) {\n        for (const pt of result[i].keypoints) {\n          ctx.fillStyle = localOptions.useDepth && pt.position.z ? `rgba(${127.5 + (2 * pt.position.z)}, ${127.5 - (2 * pt.position.z)}, 255, 0.5)` : localOptions.color;\n          ctx.fillText(`${pt.part}`, pt.position.x + 4, pt.position.y + 4);\n        }\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints) {\n      let part;\n      const points: any[] = [];\n      // shoulder line\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // torso main\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      if (points.length === 4) lines(ctx, points, localOptions); // only draw if we have complete torso\n      // leg left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // leg right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightHip');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightHeel');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightFoot');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm left\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'leftPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // arm right\n      points.length = 0;\n      part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      part = result[i].keypoints.find((a) => a.part === 'rightPalm');\n      if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);\n      curves(ctx, points, localOptions);\n      // draw all\n    }\n  }\n}\n\nexport async function hand(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText('hand', h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText('hand', h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.landmarks && h.landmarks.length > 0) {\n        for (const pt of h.landmarks) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * pt[2])}, ${127.5 - (2 * pt[2])}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawPolygons) {\n      const addPart = (part) => {\n        if (!part) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.lineWidth = localOptions.lineWidth;\n          ctx.beginPath();\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      addPart(h.annotations.indexFinger);\n      addPart(h.annotations.middleFinger);\n      addPart(h.annotations.ringFinger);\n      addPart(h.annotations.pinky);\n      addPart(h.annotations.thumb);\n      // addPart(hand.annotations.palmBase);\n    }\n  }\n}\n\nexport async function object(inCanvas: HTMLCanvasElement, result: Array<any>, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  const ctx = inCanvas.getContext('2d');\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      if (localOptions.useRawBoxes) rect(ctx, inCanvas.width * h.boxRaw[0], inCanvas.height * h.boxRaw[1], inCanvas.width * h.boxRaw[2], inCanvas.height * h.boxRaw[3], localOptions);\n      else rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${Math.round(100 * h.score)}% ${h.label}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\nexport async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {\n  if (!inCanvas || !outCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;\n  const outCtx = inCanvas.getContext('2d');\n  outCtx?.drawImage(inCanvas, 0, 0);\n}\n\nexport async function all(inCanvas: HTMLCanvasElement, result:any, drawOptions?: DrawOptions) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (!(inCanvas instanceof HTMLCanvasElement)) return;\n  face(inCanvas, result.face, localOptions);\n  body(inCanvas, result.body, localOptions);\n  hand(inCanvas, result.hand, localOptions);\n  gesture(inCanvas, result.gesture, localOptions);\n  object(inCanvas, result.object, localOptions);\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/**\n * Configuration interface definition for **Human** library\n *\n * Contains all configurable parameters\n */\nexport interface Config {\n  /** Backend used for TFJS operations */\n  backend: null | '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow',\n  /** Path to *.wasm files if backend is set to `wasm` */\n  wasmPath: string,\n  /** Print debug statements to console */\n  debug: boolean,\n  /** Perform model loading and inference concurrently or sequentially */\n  async: boolean,\n  /** Collect and print profiling data during inference operations */\n  profile: boolean,\n  /** Internal: Use aggressive GPU memory deallocator when backend is set to `webgl` or `humangl` */\n  deallocate: boolean,\n  /** Internal: Run all inference operations in an explicit local scope run to avoid memory leaks */\n  scoped: boolean,\n  /** Perform additional optimizations when input is video,\n   * - must be disabled for images\n   * - automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n   * - skips boundary detection for every `skipFrames` frames specified for each model\n   * - while maintaining in-box detection since objects don't change definition as fast */\n  videoOptimized: boolean,\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n  */\n  warmup: 'none' | 'face' | 'full' | 'body',\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are joined to this path\n  */\n  modelBasePath: string,\n  /** Run input through image filters before inference\n   * - image filters run with near-zero latency as they are executed on the GPU\n  */\n  filter: {\n    enabled: boolean,\n    /** Resize input width\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    width: number,\n    /** Resize input height\n    * - if both width and height are set to 0, there is no resizing\n    * - if just one is set, second one is scaled automatically\n    * - if both are set, values are used as-is\n    */\n    height: number,\n    /** Return processed canvas imagedata in result */\n    return: boolean,\n    /** Range: -1 (darken) to 1 (lighten) */\n    brightness: number,\n    /** Range: -1 (reduce contrast) to 1 (increase contrast) */\n    contrast: number,\n    /** Range: 0 (no sharpening) to 1 (maximum sharpening) */\n    sharpness: number,\n    /** Range: 0 (no blur) to N (blur radius in pixels) */\n    blur: number\n    /** Range: -1 (reduce saturation) to 1 (increase saturation) */\n    saturation: number,\n    /** Range: 0 (no change) to 360 (hue rotation in degrees) */\n    hue: number,\n    /** Image negative */\n    negative: boolean,\n    /** Image sepia colors */\n    sepia: boolean,\n    /** Image vintage colors */\n    vintage: boolean,\n    /** Image kodachrome colors */\n    kodachrome: boolean,\n    /** Image technicolor colors */\n    technicolor: boolean,\n    /** Image polaroid camera effect */\n    polaroid: boolean,\n    /** Range: 0 (no pixelate) to N (number of pixels to pixelate) */\n    pixelate: number,\n  },\n  /** Controlls gesture detection */\n  gesture: {\n    enabled: boolean,\n  },\n  /** Controlls and configures all face-specific options:\n   * - face detection, face mesh detection, age, gender, emotion detection and face description\n   * Parameters:\n   * - enabled: true/false\n   * - modelPath: path for individual face model\n   * - rotation: use calculated rotated face image or just box with rotation as-is, false means higher performance, but incorrect mesh mapping on higher face angles\n   * - maxFaces: maximum number of faces detected in the input, should be set to the minimum number for performance\n   * - skipFrames: how many frames to go without re-running the face detector and just run modified face mesh analysis, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no faces detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - return extracted face as tensor for futher user processing\n  */\n  face: {\n    enabled: boolean,\n    detector: {\n      modelPath: string,\n      rotation: boolean,\n      maxFaces: number,\n      skipFrames: number,\n      skipInitial: boolean,\n      minConfidence: number,\n      iouThreshold: number,\n      scoreThreshold: number,\n      return: boolean,\n    },\n    mesh: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    iris: {\n      enabled: boolean,\n      modelPath: string,\n    },\n    description: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    age: {\n      enabled: boolean,\n      modelPath: string,\n      skipFrames: number,\n    },\n    gender: {\n      enabled: boolean,\n      minConfidence: number,\n      modelPath: string,\n      skipFrames: number,\n    },\n    emotion: {\n      enabled: boolean,\n      minConfidence: number,\n      skipFrames: number,\n      modelPath: string,\n    },\n    embedding: {\n      enabled: boolean,\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all body detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - maxDetections: maximum number of people detected in the input, should be set to the minimum number for performance\n   * - scoreThreshold: threshold for deciding when to remove people based on score in non-maximum suppression\n   * - nmsRadius: threshold for deciding whether body parts overlap too much in non-maximum suppression\n  */\n  body: {\n    enabled: boolean,\n    modelPath: string,\n    maxDetections: number,\n    scoreThreshold: number,\n    nmsRadius: number,\n  },\n  /** Controlls and configures all hand detection specific options\n   * - enabled: true/false\n   * - modelPath: paths for both hand detector model and hand skeleton model\n   * - rotation: use best-guess rotated hand image or just box with rotation as-is, false means higher performance, but incorrect finger mapping if hand is inverted\n   * - skipFrames: how many frames to go without re-running the hand bounding box detector and just run modified hand skeleton detector, only valid if videoOptimized is set to true\n   * - skipInitial: if previous detection resulted in no hands detected, should skipFrames be reset immediately to force new detection cycle\n   * - minConfidence: threshold for discarding a prediction\n   * - iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression\n   * - scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression\n   * - maxHands: maximum number of hands detected in the input, should be set to the minimum number for performance\n   * - landmarks: detect hand landmarks or just hand boundary box\n  */\n  hand: {\n    enabled: boolean,\n    rotation: boolean,\n    skipFrames: number,\n    skipInitial: boolean,\n    minConfidence: number,\n    iouThreshold: number,\n    scoreThreshold: number,\n    maxHands: number,\n    landmarks: boolean,\n    detector: {\n      modelPath: string,\n    },\n    skeleton: {\n      modelPath: string,\n    },\n  },\n  /** Controlls and configures all object detection specific options\n   * - minConfidence: minimum score that detection must have to return as valid object\n   * - iouThreshold: ammount of overlap between two detected objects before one object is removed\n   * - maxResults: maximum number of detections to return\n   * - skipFrames: run object detection every n input frames, only valid if videoOptimized is set to true\n  */\n  object: {\n    enabled: boolean,\n    modelPath: string,\n    minConfidence: number,\n    iouThreshold: number,\n    maxResults: number,\n    skipFrames: number,\n  },\n}\n\nconst config: Config = {\n  backend: 'webgl',          // select tfjs backend to use\n                             // can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl\n                             // leave as empty string to continue using default backend\n                             // when backend is set outside of Human library\n  modelBasePath: '../models/', // base path for all models\n  wasmPath: '../assets/',    // path for wasm binaries\n                             // only used for backend: wasm\n  debug: true,               // print additional status messages to console\n  async: true,               // execute enabled models in parallel\n                             // this disables per-model performance data but\n                             // slightly increases performance\n                             // cannot be used if profiling is enabled\n  profile: false,            // internal: enable tfjs profiling\n                             // this has significant performance impact\n                             // only enable for debugging purposes\n                             // currently only implemented for age,gender,emotion models\n  deallocate: false,         // internal: aggresively deallocate gpu memory after each usage\n                             // only valid for webgl and humangl backend and only during first call\n                             // cannot be changed unless library is reloaded\n                             // this has significant performance impact\n                             // only enable on low-memory devices\n  scoped: false,             // internal: enable scoped runs\n                             // some models *may* have memory leaks,\n                             // this wrapps everything in a local scope at a cost of performance\n                             // typically not needed\n  videoOptimized: true,      // perform additional optimizations when input is video,\n                             // must be disabled for images\n                             // automatically disabled for Image, ImageData, ImageBitmap and Tensor inputs\n                             // skips boundary detection for every n frames\n                             // while maintaining in-box detection since objects cannot move that fast\n  warmup: 'face',            // what to use for human.warmup(), can be 'none', 'face', 'full'\n                             // warmup pre-initializes all models for faster inference but can take\n                             // significant time on startup\n                             // only used for `webgl` and `humangl` backends\n  filter: {                  // run input through image filters before inference\n                             // image filters run with near-zero latency as they are executed on the GPU\n    enabled: true,           // enable image pre-processing filters\n    width: 0,                // resize input width\n    height: 0,               // resize input height\n                             // if both width and height are set to 0, there is no resizing\n                             // if just one is set, second one is scaled automatically\n                             // if both are set, values are used as-is\n    return: true,            // return processed canvas imagedata in result\n    brightness: 0,           // range: -1 (darken) to 1 (lighten)\n    contrast: 0,             // range: -1 (reduce contrast) to 1 (increase contrast)\n    sharpness: 0,            // range: 0 (no sharpening) to 1 (maximum sharpening)\n    blur: 0,                 // range: 0 (no blur) to N (blur radius in pixels)\n    saturation: 0,           // range: -1 (reduce saturation) to 1 (increase saturation)\n    hue: 0,                  // range: 0 (no change) to 360 (hue rotation in degrees)\n    negative: false,         // image negative\n    sepia: false,            // image sepia colors\n    vintage: false,          // image vintage colors\n    kodachrome: false,       // image kodachrome colors\n    technicolor: false,      // image technicolor colors\n    polaroid: false,         // image polaroid camera effect\n    pixelate: 0,             // range: 0 (no pixelate) to N (number of pixels to pixelate)\n  },\n\n  gesture: {\n    enabled: true,           // enable simple gesture recognition\n  },\n\n  face: {\n    enabled: true,           // controls if specified modul is enabled\n                             // face.enabled is required for all face models:\n                             // detector, mesh, iris, age, gender, emotion\n                             // (note: module is not loaded until it is required)\n    detector: {\n      modelPath: 'blazeface-back.json', // detector model\n                             // can be either absolute path or relative to modelBasePath\n      rotation: false,       // use best-guess rotated face image or just box with rotation as-is\n                             // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n                             // this parameter is not valid in nodejs\n      maxFaces: 10,          // maximum number of faces detected in the input\n                             // should be set to the minimum number for performance\n      skipFrames: 21,        // how many frames to go without re-running the face bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated face analysis as the head probably hasn't moved much\n                             // in short time (10 * 1/25 = 0.25 sec)\n      skipInitial: false,    // if previous detection resulted in no faces detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n      minConfidence: 0.2,    // threshold for discarding a prediction\n      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in\n                             // non-maximum suppression (0.1 means drop if overlap 10%)\n      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression,\n                             // this is applied on detection objects only and before minConfidence\n      return: false,         // return extracted face as tensor\n    },\n\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',  // facemesh model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',  // face iris model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    description: {\n      enabled: true,         // to improve accuracy of face description extraction it is\n                             // recommended to enable detector.rotation and mesh.enabled\n      modelPath: 'faceres.json',  // face description model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 31,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      skipFrames: 32,        // how many frames to go without re-running the detector\n      modelPath: 'emotion.json',  // face emotion model\n                             // can be either absolute path or relative to modelBasePath\n    },\n\n    age: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'age.json',  // age model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 33,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    gender: {\n      enabled: false,        // obsolete, replaced by description module\n      minConfidence: 0.1,    // threshold for discarding a prediction\n      modelPath: 'gender.json',  // gender model\n                             // can be either absolute path or relative to modelBasePath\n      skipFrames: 34,        // how many frames to go without re-running the detector\n                             // only used for video inputs\n    },\n\n    embedding: {\n      enabled: false,        // obsolete, replaced by description module\n      modelPath: 'mobileface.json',  // face descriptor model\n                             // can be either absolute path or relative to modelBasePath\n                            },\n  },\n\n  body: {\n    enabled: true,\n    modelPath: 'posenet.json',  // body model\n                             // can be either absolute path or relative to modelBasePath\n                             // can be 'posenet', 'blazepose' or 'efficientpose'\n                             // 'blazepose' and 'efficientpose' are experimental\n    maxDetections: 10,       // maximum number of people detected in the input\n                             // should be set to the minimum number for performance\n                             // only valid for posenet as blazepose only detects single pose\n    scoreThreshold: 0.3,     // threshold for deciding when to remove boxes based on score\n                             // in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression\n                             // only valid for posenet as blazepose only detects single pose\n  },\n\n  hand: {\n    enabled: true,\n    rotation: false,         // use best-guess rotated hand image or just box with rotation as-is\n                             // false means higher performance, but incorrect finger mapping if hand is inverted\n    skipFrames: 12,          // how many frames to go without re-running the hand bounding box detector\n                             // only used for video inputs\n                             // e.g., if model is running st 25 FPS, we can re-use existing bounding\n                             // box for updated hand skeleton analysis as the hand probably\n                             // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n    skipInitial: false,      // if previous detection resulted in no hands detected,\n                             // should skipFrames be reset immediately to force new detection cycle\n    minConfidence: 0.1,      // threshold for discarding a prediction\n    iouThreshold: 0.1,       // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on\n                             // score in non-maximum suppression\n    maxHands: 1,             // maximum number of hands detected in the input\n                             // should be set to the minimum number for performance\n    landmarks: true,         // detect hand landmarks or just hand boundary box\n    detector: {\n      modelPath: 'handdetect.json',  // hand detector model\n                             // can be either absolute path or relative to modelBasePath\n    },\n    skeleton: {\n      modelPath: 'handskeleton.json',  // hand skeleton model\n                             // can be either absolute path or relative to modelBasePath\n    },\n  },\n\n  object: {\n    enabled: false,\n    modelPath: 'nanodet.json',  // object detection model\n                             // can be either absolute path or relative to modelBasePath\n                             // 'nanodet' is experimental\n    minConfidence: 0.20,     // threshold for discarding a prediction\n    iouThreshold: 0.40,      // threshold for deciding whether boxes overlap too much\n                             // in non-maximum suppression\n    maxResults: 10,          // maximum number of objects detected in the input\n    skipFrames: 41,          // how many frames to go without re-running the detector\n  },\n};\nexport { config as defaults };\n", "// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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   "names": []
 }
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index 0e6bfc7d..901bff01 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -5,31 +5,31 @@
   author: <https://github.com/vladmandic>'
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this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let l,u=ew(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(ew(e)){let{kernelName:d,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Sm(d,this.backendName);A(x!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();l=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let _=w.map(k=>{if(k.rank!=null)return k;let{dataId:D,shape:T,dtype:R}=k;return this.makeTensorFromDataId(D,T,R)});if(n){let k=this.getTensorsForGradient(d,h,_);o=this.saveTensorsForBackwardMode(k)}return _}}else{let{forwardFunc:d}=e,h=g=>{!n||(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();l=this.tidy(()=>d(this.backend,h));let x=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:p}=e,m=ew(e)?null:e.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(f=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs)}),n&&this.addTapeNode(u,c,t,m,o,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Ub(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let 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o=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(o=e.size*Uh(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof nl||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let o=e.size*Uh(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},l=Ub(e);l!=null&&(n=l.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,p)=>{if(c==null){let m=o[p],f=pc(m.size,m.dtype);return this.makeTensor(f,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Dm(e),o=new Set(t.map(s=>s.id));for(let s=0;s<this.state.activeScope.track.length;s++){let a=this.state.activeScope.track[s];!a.kept&&!o.has(a.id)&&a.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(s=>{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(A(t.length>0,()=>"gradients() received an empty list of xs."),o!=null&&o.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));A(s instanceof Ve,()=>"The result y returned by f() must be a tensor.");let a=uS(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[s.id]=o==null?wG(s.shape):o,cS(i,a,u=>this.tidy(u),_G);let l=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:l}})}customGrad(e){return A(Rs(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{A(t.every(i=>i instanceof Ve),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let o,n={};t.forEach((i,l)=>{n[l]=i});let s=(i,l)=>(o=e(...t,l),A(o.value instanceof Ve,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),A(Rs(o.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),o.value),a=(i,l)=>{let u=o.gradFunc(i,l),c=Array.isArray(u)?u:[u];A(c.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),A(c.every(m=>m instanceof Ve),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let p={};return c.forEach((m,f)=>{p[f]=()=>m}),p};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}async time(e){let t=Vl(),o=await this.backend.time(e);return o.wallMs=Vl()-t,o}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new tw;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Wl.nextTensorId=0;Wl.nextVariableId=0;function wG(r){let e=bm(ct(r),"float32");return E.makeTensor(e,r,"float32")}function rw(){let r=Wb();if(r._tfengine==null){let e=new Hh(r);r._tfengine=new Wl(e)}return nS(r._tfengine.ENV),dS(()=>r._tfengine),r._tfengine}var E=rw();function _G(r,e){let t={a:r,b:e};return E.runKernel(Fo,t)}var jl={};Ge(jl,{isBrowser:()=>ow,isMobile:()=>vG});function kG(){return typeof navigator!="undefined"&&navigator!=null}function vG(){if(kG()){let r=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(r)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(r.substr(0,4))}return!1}function ow(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var ms=W();ms.registerFlag("DEBUG",()=>!1,r=>{r&&console.warn("Debugging mode is ON. 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To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await ei().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(ei().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return qc.print(this,e)}clone(){return this.throwIfDisposed(),qc.clone(this)}toString(e=!1){let t=this.dataSync();return NS(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),qc.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),ei().makeVariable(this,e,t,n)}};Object.defineProperty(Oe,Symbol.hasInstance,{value:r=>!!r&&r.data!=null&&r.dataSync!=null&&r.throwIfDisposed!=null});function F(){return Nm("Tensor",()=>Oe)}F();var ul=class extends Oe{constructor(e,t,n,o){super(e.shape,e.dtype,e.dataId,o);this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Kr(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);ei().disposeTensor(this),this.dataId=e.dataId,ei().incRef(this,null)}dispose(){ei().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(ul,Symbol.hasInstance,{value:r=>r instanceof Oe&&r.assign!=null&&r.assign instanceof Function});var Zn={};We(Zn,{assertTypesMatch:()=>pw,getTensorsInContainer:()=>Om,isTensorInList:()=>MG,makeTypesMatch:()=>je});var iw;(function(r){r.R0="R0",r.R1="R1",r.R2="R2",r.R3="R3",r.R4="R4",r.R5="R5",r.R6="R6"})(iw||(iw={}));var aw;(function(r){r.float32="float32",r.int32="int32",r.bool="int32",r.complex64="complex64"})(aw||(aw={}));var lw;(function(r){r.float32="float32",r.int32="int32",r.bool="bool",r.complex64="complex64"})(lw||(lw={}));var uw;(function(r){r.float32="float32",r.int32="float32",r.bool="float32",r.complex64="complex64"})(uw||(uw={}));var cw;(function(r){r.float32="complex64",r.int32="complex64",r.bool="complex64",r.complex64="complex64"})(cw||(cw={}));var PG={float32:uw,int32:aw,bool:lw,complex64:cw};function ar(r,e){if(r==="string"||e==="string"){if(r==="string"&&e==="string")return"string";throw new Error(`Can not upcast ${r} with ${e}`)}return PG[r][e]}function Kl(r){return ar(r,"int32")}function je(r,e){if(r.dtype===e.dtype)return[r,e];let t=ar(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function pw(r,e){T(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function MG(r,e){return e.some(t=>t.id===r.id)}function Om(r){let e=[],t=new Set;return DS(r,e,t),e}function DS(r,e,t){if(r==null)return;if(r instanceof Oe){e.push(r);return}if(!LG(r))return;let n=r;for(let o in n){let s=n[o];t.has(s)||(t.add(s),DS(s,e,t))}}function LG(r){return Array.isArray(r)||typeof r=="object"}function mw(r){return r.kernelName!=null}var fw=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Xl=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new fw}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. 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o=++this.pendingBackendInitId,s=n.then(a=>o<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(o<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:o,asyncInit:s}=this.initializeBackend(n);if(s||o)return{name:n,asyncInit:s}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),o=n.backend,s=this.readSync(t),a=o.refCount(t);o.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function 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this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let o=this.backend.numDataIds(),s=0;n.forEach(l=>{s+=l.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=o-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],o=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let l,u=mw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(mw(e)){let{kernelName:d,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let y=Dm(d,this.backendName);T(y!=null,()=>`Cannot find registered kernel '${d}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();l=y.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let _=w.map(I=>{if(I.rank!=null)return I;let{dataId:E,shape:$,dtype:D}=I;return this.makeTensorFromDataId(E,$,D)});if(o){let I=this.getTensorsForGradient(d,h,_);n=this.saveTensorsForBackwardMode(I)}return _}}else{let{forwardFunc:d}=e,h=g=>{!o||(n=g.map(y=>this.keep(this.clone(y))))};i=()=>{let g=this.backend.numDataIds();l=this.tidy(()=>d(this.backend,h));let y=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,y),y}}let{inputs:c,attrs:p}=e,m=mw(e)?null:e.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(f=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs)}),o&&this.addTapeNode(u,c,t,m,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let o=nw(e);if(o!=null){let s=o.inputsToSave||[],a=o.outputsToSave||[],i;o.saveAllInputs?(T(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let l=n.filter((u,c)=>a[c]);return i.concat(l)}return[]}makeTensor(e,t,n,o){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",o=o||this.backend;let s=e;n==="string"&&bo(e[0])&&(s=e.map(l=>ll(l)));let a=o.write(s,t,n),i=new Oe(t,n,a,this.nextTensorId());if(this.trackTensor(i,o),n==="string"){let l=this.state.tensorInfo.get(a),u=Jb(s);this.state.numBytes+=u-l.bytes,l.bytes=u}return i}makeTensorFromDataId(e,t,n,o){n=n||"float32";let s=new Oe(t,n,e,this.nextTensorId());return this.trackTensor(s,o),s}makeVariable(e,t=!0,n,o){n=n||this.nextVariableId().toString(),o!=null&&o!==e.dtype&&(e=e.cast(o));let s=new ul(e,t,n,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Xh(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ul||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Xh(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(o=>o.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let o of 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a=k(r,"x","batchNorm"),i=k(e,"mean","batchNorm"),l=k(t,"variance","batchNorm"),u;o!=null&&(u=k(o,"scale","batchNorm"));let c;return n!=null&&(c=k(n,"offset","batchNorm")),T(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),T(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),T(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&T(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&T(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Jn(a,i,l,c,u,s)}var Vw=S({batchNorm4d_:kj});function vj(r,e,t){let n=k(r,"x","bincount"),o=k(e,"weights","bincount");T(n.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${n.dtype}`),T(t>=0,()=>`size must be non-negative, but got 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qm=S({ceil_:Ij});function Nj(r,e,t){let n=k(r,"x","clipByValue");T(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`);let o={x:n},s={clipValueMin:e,clipValueMax:t};return A.runKernel(Kn,o,s)}var lr=S({clipByValue_:Nj});function Sj(r){return Qe(r,0)}var Ww=S({concat1d_:Sj});function Tj(r,e){return Qe(r,e)}var jw=S({concat2d_:Tj});function Aj(r,e){return Qe(r,e)}var Uw=S({concat3d_:Aj});function Ej(r,e){return Qe(r,e)}var Hw=S({concat4d_:Ej});function Dj(r,e,t,n,o="NHWC",s=[1,1],a){let i=k(r,"x","conv2d"),l=k(e,"filter","conv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=L(i,[1,i.shape[0],i.shape[1],i.shape[2]])),T(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),T(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),a!=null&&T(ot(n),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let p=o==="NHWC"?u.shape[3]:u.shape[1];T(p===l.shape[2],()=>`Error in conv2d: depth 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p={x:u,filter:l},m={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=E.runKernel(Dn,p,m);return c?L(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ds=S({depthwiseConv2d_:Ij});function Nj(r){let t={x:v(r,"x","diag")};return E.runKernel(Cc,t)}var Sj=S({diag_:Nj});function Tj(r,e,t,o,n=[1,1],s="NHWC"){let a=v(r,"x","dilation2d"),i=v(e,"filter","dilation2d");A(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),A(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),A(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=a,u=!1;a.rank===3&&(l=L(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:l,filter:i},p={strides:t,pad:o,dilations:n},m=E.runKernel(qa,c,p);return u?L(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Hm=S({dilation2d_:Tj});function Aj(r,e){let t=r.length,o=[];for(let n=0;n<t;n++){let 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t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};lp.className="Adam";co(lp);var up=class extends zr{constructor(e,t,o,n=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=ce(1,this.accBeta1),n=me(-this.learningRate,ee(P(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ce(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ce(i).variable(l)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedWeightedInfNorm[a].variable,m=ee(P(c,this.beta1),P(u,1-this.beta1)),f=P(p,this.beta2),d=It(u),h=eo(f,d);c.assign(m),p.assign(h);let g=ee(P(me(n,o),me(m,ee(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(ee(this.iteration,1)),this.accBeta1.assign(P(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ae(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};up.className="Adamax";co(up);var pl=class extends zr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=E.registeredVariables[o];V(()=>{let i=ee(P(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Et(le(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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s=E.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:V(()=>Ce(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:V(()=>Ce(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:V(()=>Ce(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let l=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;V(()=>{let c=ee(P(l,this.decay),P(Oe(i),1-this.decay));if(this.centered){let p=this.accumulatedMeanGrads[n].variable,m=ee(P(p,this.decay),P(i,1-this.decay)),f=me(P(i,this.learningRate),gt(ce(c,ee(Oe(m),this.epsilon)))),d=ee(P(u,this.momentum),f);l.assign(c),p.assign(m),u.assign(d);let h=ce(s,d);s.assign(h)}else{let p=ee(P(l,this.decay),P(Oe(i),1-this.decay)),m=ee(P(u,this.momentum),me(P(i,this.learningRate),gt(ee(p,this.epsilon))));l.assign(p),u.assign(m);let f=ce(s,m);s.assign(f)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ae(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ae(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ae(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};pp.className="RMSProp";co(pp);var _a=class{static sgd(e){return new pl(e)}static momentum(e,t,o=!1){return new cp(e,t,o)}static rmsprop(e,t=.9,o=0,n=null,s=!1){return new pp(e,t,o,n,s)}static adam(e=.001,t=.9,o=.999,n=null){return new lp(e,t,o,n)}static adadelta(e=.001,t=.95,o=null){return new ip(e,t,o)}static adamax(e=.002,t=.9,o=.999,n=null,s=0){return new up(e,t,o,n,s)}static 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a=n.size/o.size,i=me(ge(s),ge(o));return a>1?me(i,le(a)):i}}if(t===Gt.SUM_BY_NONZERO_WEIGHTS){if(o==null)return me(ge(s),le(n.size));{let a=P(o,Qt(n.shape)),i=oe(ge(to(a,le(0))),"float32");return me(ge(s),i)}}throw Error(`Unknown reduction: ${t}`)}var Er=S({computeWeightedLoss_:uq});function cq(r,e,t,n=Gt.SUM_BY_NONZERO_WEIGHTS){let o=k(r,"labels","absoluteDifference"),s=k(e,"predictions","absoluteDifference"),a=null;t!=null&&(a=k(t,"weights","absoluteDifference")),At(o.shape,s.shape,"Error in absoluteDifference: ");let i=Nt(pe(o,s));return Er(i,a,n)}var k1=S({absoluteDifference_:cq});function pq(r,e,t,n,o=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","cosineDistance"),a=k(e,"predictions","cosineDistance"),i=null;n!=null&&(i=k(n,"weights","cosineDistance")),At(s.shape,a.shape,"Error in cosineDistance: ");let l=le(1),u=pe(l,ge(P(s,a),t,!0));return Er(u,i,o)}var v1=S({cosineDistance_:pq});function mq(r,e,t,n=Gt.SUM_BY_NONZERO_WEIGHTS){let o=k(r,"labels","hingeLoss"),s=k(e,"predictions","hingeLoss"),a=null;t!=null&&(a=k(t,"weights","hingeLoss")),At(o.shape,s.shape,"Error in hingeLoss: ");let i=le(1);o=pe(P(le(2),o),i);let l=Ar(pe(i,P(o,s)));return Er(l,a,n)}var C1=S({hingeLoss_:mq});function fq(r,e,t,n=1,o=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","huberLoss"),a=k(e,"predictions","huberLoss"),i=null;t!=null&&(i=k(t,"weights","huberLoss")),At(s.shape,a.shape,"Error in huberLoss: ");let l=le(n),u=Nt(pe(a,s)),c=bs(u,l),p=pe(u,c),m=ee(P(le(.5),Pe(c)),P(l,p));return Er(m,i,o)}var I1=S({huberLoss_:fq});function dq(r,e,t,n=1e-7,o=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"labels","logLoss"),a=k(e,"predictions","logLoss"),i=null;t!=null&&(i=k(t,"weights","logLoss")),At(s.shape,a.shape,"Error in logLoss: ");let l=le(1),u=le(n),c=qe(P(s,cr(ee(a,u)))),p=P(pe(l,s),cr(ee(pe(l,a),u))),m=pe(c,p);return Er(m,i,o)}var N1=S({logLoss_:dq});function hq(r,e,t,n=Gt.SUM_BY_NONZERO_WEIGHTS){let o=k(r,"labels","meanSquaredError"),s=k(e,"predictions","meanSquaredError"),a=null;t!=null&&(a=k(t,"weights","meanSquaredError")),At(o.shape,s.shape,"Error in meanSquaredError: ");let i=bu(o,s);return Er(i,a,n)}var S1=S({meanSquaredError_:hq});function gq(r,e){let t=k(r,"labels","sigmoidCrossEntropyWithLogits"),n=k(e,"logits","sigmoidCrossEntropyWithLogits");At(t.shape,n.shape,"Error in sigmoidCrossEntropyWithLogits: ");let o=Ar(n),s=P(n,t),a=lu(Jt(qe(Nt(n))));return ee(pe(o,s),a)}function xq(r,e,t,n=0,o=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"multiClassLabels","sigmoidCrossEntropy"),a=k(e,"logits","sigmoidCrossEntropy"),i=null;if(t!=null&&(i=k(t,"weights","sigmoidCrossEntropy")),At(s.shape,a.shape,"Error in sigmoidCrossEntropy: "),n>0){let u=le(n),c=le(1),p=le(.5);s=ee(P(s,pe(c,u)),P(p,u))}let l=gq(s,a);return Er(l,i,o)}var T1=S({sigmoidCrossEntropy_:xq});function yq(r,e,t=-1){if(t===-1&&(t=e.rank-1),t!==e.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${e.rank} and dim was ${t}`);return Qr((o,s,a)=>{let l=nf(s,[t],!0),u=pe(oe(s,"float32"),l);a([o,u]);let c=qe(P(u,o));return{value:ge(c,[t]),gradFunc:(f,d)=>{let[h,g]=d,y=eo(f.shape,[t]);return[P(L(f,y),pe(oe(h,"float32"),Jt(g))),P(L(f,y),pe(Jt(g),oe(h,"float32")))]}}})(r,e)}function bq(r,e,t,n=0,o=Gt.SUM_BY_NONZERO_WEIGHTS){let s=k(r,"onehotLabels","softmaxCrossEntropy"),a=k(e,"logits","softmaxCrossEntropy"),i=null;if(t!=null&&(i=k(t,"weights","softmaxCrossEntropy")),At(s.shape,a.shape,"Error in softmaxCrossEntropy: "),n>0){let u=le(n),c=le(1),p=le(s.shape[1]);s=ee(P(s,pe(c,u)),me(u,p))}let l=yq(s,a);return Er(l,i,o)}var A1=S({softmaxCrossEntropy_:bq});var gFe={fft:Ca,ifft:oi,rfft:Ia,irfft:yu},xFe={hammingWindow:o1,hannWindow:Ig,frame:Ng,stft:s1},ii={flipLeftRight:a1,resizeNearestNeighbor:Dg,resizeBilinear:Eg,rotateWithOffset:l1,cropAndResize:i1,nonMaxSuppression:u1,nonMaxSuppressionAsync:m1,nonMaxSuppressionWithScore:f1,nonMaxSuppressionWithScoreAsync:d1,nonMaxSuppressionPadded:h1,nonMaxSuppressionPaddedAsync:g1,transform:x1},E1={bandPart:y1,gramSchmidt:b1,qr:_1},yFe={absoluteDifference:k1,computeWeightedLoss:Er,cosineDistance:v1,hingeLoss:C1,huberLoss:I1,logLoss:N1,meanSquaredError:S1,sigmoidCrossEntropy:T1,softmaxCrossEntropy:A1};var zr=class extends pg{minimize(e,t=!1,n){let{value:o,grads:s}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ae(s),t?o:(o.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return hg(e,t)}dispose(){this.iterations_!=null&&Ae(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:le(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(zr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var up=class extends zr{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=A.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=A.registeredVariables[n],a=!1;this.accumulatedGrads[o]==null&&(this.accumulatedGrads[o]={originalName:`${n}/accum_grad`,variable:V(()=>Ie(s).variable(a))}),this.accumulatedUpdates[o]==null&&(this.accumulatedUpdates[o]={originalName:`${n}/accum_var`,variable:V(()=>Ie(s).variable(a))});let i=Array.isArray(e)?e[o].tensor:e[n];if(i==null)return;let l=this.accumulatedGrads[o].variable,u=this.accumulatedUpdates[o].variable;V(()=>{let c=ee(P(l,this.rho),P(Pe(i),1-this.rho)),p=P(me(gt(ee(u,this.epsilon)),gt(ee(l,this.epsilon))),i),m=ee(P(u,this.rho),P(Pe(p),1-this.rho));l.assign(c),u.assign(m);let f=ee(P(p,-this.learningRate),s);s.assign(f)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ae(this.accumulatedGrads.map(e=>e.variable)),Ae(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};up.className="Adadelta";fn(up);var cp=class extends zr{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=A.registeredVariables[n];if(this.accumulatedGrads[o]==null){let l=!1;this.accumulatedGrads[o]={originalName:`${n}/accumulator`,variable:V(()=>xa(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[o].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[o].variable;V(()=>{let l=ee(i,Pe(a));i.assign(l);let u=ee(P(me(a,gt(ee(l,A.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ae(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};cp.className="Adagrad";fn(cp);var pp=class extends zr{constructor(e,t,n,o=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=le(t).variable(),this.accBeta2=le(n).variable()}),o==null&&(this.epsilon=A.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=pe(1,this.accBeta1),o=pe(1,this.accBeta2);t.forEach((s,a)=>{let i=A.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:V(()=>Ie(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:V(()=>Ie(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=ee(P(c,this.beta1),P(u,1-this.beta1)),f=ee(P(p,this.beta2),P(Pe(u),1-this.beta2)),d=me(m,n),h=me(f,o);c.assign(m),p.assign(f);let g=ee(P(me(d,ee(gt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(P(this.accBeta1,this.beta1)),this.accBeta2.assign(P(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ae(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(Lr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Lr(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(o=>({originalName:o.name,variable:o.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(o=>({originalName:o.name,variable:o.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};pp.className="Adam";fn(pp);var mp=class extends zr{constructor(e,t,n,o=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=o,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=le(0).variable(),this.accBeta1=le(t).variable()}),o==null&&(this.epsilon=A.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=pe(1,this.accBeta1),o=me(-this.learningRate,ee(P(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=A.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Ie(i).variable(l)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Ie(i).variable(l)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedWeightedInfNorm[a].variable,m=ee(P(c,this.beta1),P(u,1-this.beta1)),f=P(p,this.beta2),d=Nt(u),h=en(f,d);c.assign(m),p.assign(h);let g=ee(P(me(o,n),me(m,ee(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(ee(this.iteration,1)),this.accBeta1.assign(P(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ae(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ae(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};mp.className="Adamax";fn(mp);var hl=class extends zr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=Array.isArray(e)?e[o].tensor:e[n];if(s==null)return;let a=A.registeredVariables[n];V(()=>{let i=ee(P(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Et(le(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};hl.className="SGD";fn(hl);var fp=class extends hl{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=le(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,o)=>{let s=A.registeredVariables[n];if(this.accumulations[o]==null){let l=!1;this.accumulations[o]={originalName:`${n}/momentum`,variable:V(()=>Ie(s).variable(l))}}let a=this.accumulations[o].variable,i=Array.isArray(e)?e[o].tensor:e[n];i!=null&&V(()=>{let l,u=ee(P(this.m,a),i);this.useNesterov?l=ee(P(this.c,ee(i,P(u,this.m))),s):l=ee(P(this.c,u),s),a.assign(u),s.assign(l)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ae(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await 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ut=0;for(let mt=ze;mt<St;mt++){let Pt=Math.min(we,g-1)*oe,_o=Math.min(we,x-1)*ue,Xt=U[Pt+Tt*Y+mt*re],io=H[mt*J+Ue*ie+_o];ut+=Xt*io}de[we*ae+(Tt*G+Ue)]+=ut}}return t.disposeIntermediateTensorInfo(T),t.disposeIntermediateTensorInfo(R),t.makeTensorInfo(_,fe.dtype,fe.values)}var q1={kernelName:In,backendName:"cpu",kernelFunc:O_};function Zq(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o,m,f,d,h=[];m=O_({inputs:{a:n,b:s},attrs:{transposeA:l,transposeB:u},backend:t}),a&&(f=ka({inputs:{a:m,b:a},backend:t}),h.push(m),m=f),c&&(d=gp(t,m,c,i,p),h.push(m),m=d);for(let x of h)t.disposeIntermediateTensorInfo(x);return m}var K1={kernelName:Ks,backendName:"cpu",kernelFunc:Zq};var Jq=$e(yi,r=>Math.acos(r)),X1={kernelName:yi,backendName:"cpu",kernelFunc:Jq};var Qq=$e(bi,r=>Math.acosh(r)),Y1={kernelName:bi,backendName:"cpu",kernelFunc:Qq};function eK(r){let{inputs:e,backend:t}=r,o=e;te(e,"addN");let n=o.map(i=>t.data.get(i.dataId).values),s=ve(o[0].shape,o[0].dtype),a=s.values;for(let i=0;i<o.length;i++){let l=n[i];for(let u=0;u<a.length;u++)a[u]+=l[u]}return t.makeTensorInfo(s.shape,s.dtype,s.values)}var Z1={kernelName:kn,backendName:"cpu",kernelFunc:eK};function tK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;te(n,"all");let i=y.parseAxisParam(s,n.shape),l=i,u=N.getAxesPermutation(l,n.shape.length),c=n;u!=null&&(c=or({inputs:{x:n},backend:t,attrs:{perm:u}}),l=N.getInnerMostAxes(l.length,n.shape.length)),N.assertAxesAreInnerMostDims("all",l,c.shape.length);let[p,m]=N.computeOutAndReduceShapes(c.shape,l),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let _=0;_<f;++_){let k=h[b+_];w=w&&k}d[x]=w}u!=null&&t.disposeIntermediateTensorInfo(c);let g=t.makeTensorInfo(p,c.dtype,d);if(a){let x=N.expandShapeToKeepDim(p,i),b=Qe({inputs:{x:g},backend:t,attrs:{shape:x}});return t.disposeIntermediateTensorInfo(g),b}return g}var J1={kernelName:mc,backendName:"cpu",kernelFunc:tK};function rK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;te(n,"any");let i=y.parseAxisParam(s,n.shape),l=i,u=N.getAxesPermutation(l,n.shape.length),c=n;u!=null&&(c=or({inputs:{x:n},backend:t,attrs:{perm:u}}),l=N.getInnerMostAxes(l.length,n.shape.length)),N.assertAxesAreInnerMostDims("any",l,c.shape.length);let[p,m]=N.computeOutAndReduceShapes(c.shape,l),f=y.sizeFromShape(m),d=y.makeZerosTypedArray(y.sizeFromShape(p),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;x<d.length;++x){let b=x*f,w=h[b];for(let _=0;_<f;++_){let k=h[b+_];w=w||k}d[x]=w}u!=null&&t.disposeIntermediateTensorInfo(c);let g=t.makeTensorInfo(p,c.dtype,d);if(a){let x=N.expandShapeToKeepDim(p,i),b=Qe({inputs:{x:g},backend:t,attrs:{shape:x}});return t.disposeIntermediateTensorInfo(g),b}return g}var Q1={kernelName:fc,backendName:"cpu",kernelFunc:rK};function oK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o;te(n,"argMax");let a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=or({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],N.assertAxesAreInnerMostDims("argMax",a,l.shape.length);let[c,p]=N.computeOutAndReduceShapes(l.shape,a),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;g<f.length;++g){let x=g*d,b=h[x],w=0;for(let _=0;_<d;++_){let k=h[x+_];k>b&&(b=k,w=_)}f[g]=w}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var eA={kernelName:vn,backendName:"cpu",kernelFunc:oK};function nK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o;te(n,"argMin");let a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=or({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],N.assertAxesAreInnerMostDims("argMin",a,l.shape.length);let[c,p]=N.computeOutAndReduceShapes(l.shape,a),m=y.sizeFromShape(c),f=y.makeZerosTypedArray(m,"int32"),d=y.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;g<f.length;++g){let x=g*d,b=h[x],w=0;for(let _=0;_<d;++_){let k=h[x+_];k<b&&(b=k,w=_)}f[g]=w}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var tA={kernelName:Ga,backendName:"cpu",kernelFunc:nK};var sK=$e(wi,r=>Math.asin(r)),rA={kernelName:wi,backendName:"cpu",kernelFunc:sK};var iK=$e(_i,r=>Math.asinh(r)),oA={kernelName:_i,backendName:"cpu",kernelFunc:iK};var aK=$e(ki,r=>Math.atan(r)),nA={kernelName:ki,backendName:"cpu",kernelFunc:aK};var lK=Xe((r,e)=>Math.atan2(r,e)),uK=et(Ci,lK),sA={kernelName:Ci,backendName:"cpu",kernelFunc:uK};var cK=$e(vi,r=>Math.atanh(r)),iA={kernelName:vi,backendName:"cpu",kernelFunc:cK};function xp(r,e,t,o,n,s){let a=n.strideHeight,i=n.strideWidth,l=n.dilationHeight,u=n.dilationWidth,c=n.effectiveFilterHeight,p=n.effectiveFilterWidth,m=n.padInfo.top,f=n.padInfo.left,d=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,h=ve(n.outShape,t),g=h.values,x=n.outShape[1]*n.outShape[2]*n.outShape[3],b=n.outShape[2]*n.outShape[3],w=n.outShape[3];for(let _=0;_<n.batchSize;++_){let k=_*x,D=_*o[0];for(let T=0;T<n.inChannels;++T)for(let R=0;R<n.outHeight;++R){let O=R*a-m,M=Math.max(0,O),G=Math.min(n.inHeight,c+O),j=k+R*b;for(let U=0;U<n.outWidth;++U){let H=U*i-f,q=Math.max(0,H),X=Math.min(n.inWidth,p+H),oe=d,Y=0,re=0;for(let ie=M;ie<G;ie+=l){let ue=D+ie*o[1];for(let ae=q;ae<X;ae+=u){let fe=ue+ae*o[2],de=r[fe+T];s==="max"&&de>oe?oe=de:s==="avg"&&(Y+=de,re++)}if(isNaN(oe))break}let 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a=n.strideDepth,i=n.strideHeight,l=n.strideWidth,u=n.dilationDepth,c=n.dilationHeight,p=n.dilationWidth,m=n.effectiveFilterDepth,f=n.effectiveFilterHeight,d=n.effectiveFilterWidth,h=n.padInfo.front,g=n.padInfo.top,x=n.padInfo.left,b=s==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=ve(n.outShape,t),_=w.values,k=n.outShape[1]*n.outShape[2]*n.outShape[3]*n.outShape[4],D=n.outShape[2]*n.outShape[3]*n.outShape[4],T=n.outShape[3]*n.outShape[4],R=n.outShape[4];for(let O=0;O<n.batchSize;++O){let M=O*k,G=O*o[0];for(let j=0;j<n.inChannels;++j)for(let U=0;U<n.outDepth;++U){let H=U*a-h,q=H;for(;q<0;)q+=u;let X=Math.min(n.inDepth,m+H),oe=M+U*D;for(let Y=0;Y<n.outHeight;++Y){let re=Y*i-g,J=re;for(;J<0;)J+=c;let ie=Math.min(n.inHeight,f+re),ue=oe+Y*T;for(let ae=0;ae<n.outWidth;++ae){let fe=ae*l-x,de=fe;for(;de<0;)de+=p;let xe=Math.min(n.inWidth,d+fe),we=ue+ae*R,De=b,Ne=0,ze=0;for(let it=q;it<X;it+=u){let St=G+it*o[1];for(let Tt=J;Tt<ie;Tt+=c){let Ue=St+Tt*o[2];for(let ut=de;ut<xe;ut+=p){let mt=Ue+ut*o[3],Pt=r[mt+j];if(s==="max"&&Pt>De?De=Pt:s==="avg"&&(Ne+=Pt,ze++),isNaN(De))break}if(isNaN(De))break}if(isNaN(De))break}let qe=we+j;_[qe]=s==="avg"?Ne/ze:De}}}}return w}function aA(r,e){let t=ve(e.outShape,"int32"),o=e.strideDepth,n=e.strideHeight,s=e.strideWidth,a=e.dilationDepth,i=e.dilationHeight,l=e.dilationWidth,u=e.effectiveFilterDepth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,m=e.padInfo.front,f=e.padInfo.top,d=e.padInfo.left;for(let h=0;h<e.batchSize;++h)for(let g=0;g<e.inChannels;++g)for(let x=0;x<e.outDepth;++x){let b=x*o-m,w=b;for(;w<0;)w+=a;let _=Math.min(e.inDepth,u+b);for(let k=0;k<e.outHeight;++k){let D=k*n-f,T=D;for(;T<0;)T+=i;let R=Math.min(e.inHeight,c+D);for(let O=0;O<e.outWidth;++O){let M=O*s-d,G=M;for(;G<0;)G+=l;let j=Math.min(e.inWidth,p+M),U=Number.NEGATIVE_INFINITY,H=-1;for(let q=w;q<_;q+=a){let X=q-b;for(let oe=T;oe<R;oe+=i){let Y=oe-D;for(let re=G;re<j;re+=l){let 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c=N.computePool3DInfo(s.shape,a,i,1,l,u),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,x=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,_=c.effectiveFilterDepth,k=c.effectiveFilterHeight,D=c.effectiveFilterWidth,T=_-1-c.padInfo.front,R=D-1-c.padInfo.left,O=k-1-c.padInfo.top,M=ve(s.shape,"float32"),G=1/(d*h*g),j=t.bufferSync(n);for(let U=0;U<c.batchSize;++U)for(let H=0;H<c.inChannels;++H)for(let q=0;q<c.inDepth;++q)for(let X=0;X<c.inHeight;++X)for(let oe=0;oe<c.inWidth;++oe){let Y=q-T,re=X-O,J=oe-R,ie=0;for(let ue=0;ue<_;ue+=x){let ae=(Y+ue)/p;if(!(ae<0||ae>=c.outDepth||Math.floor(ae)!==ae))for(let fe=0;fe<k;fe+=b){let de=(re+fe)/m;if(!(de<0||de>=c.outHeight||Math.floor(de)!==de))for(let xe=0;xe<D;xe+=w){let we=(J+xe)/f;if(we<0||we>=c.outWidth||Math.floor(we)!==we)continue;ie+=j.get(U,ae,de,we,H)}}}M.set(ie*G,U,q,X,oe,H)}return t.makeTensorInfo(M.shape,M.dtype,M.values)}var cA={kernelName:hc,backendName:"cpu",kernelFunc:fK};function dK(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;te([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,_=x-1-c.padInfo.top,k=ve(a.shape,"float32"),D=1/(f*d),T=t.data.get(n.dataId).values,R=ve(n.shape,"float32",T);for(let O=0;O<c.batchSize;++O)for(let M=0;M<c.inChannels;++M)for(let G=0;G<c.inHeight;++G)for(let j=0;j<c.inWidth;++j){let U=G-_,H=j-w,q=0;for(let X=0;X<x;X+=h){let oe=(U+X)/p;if(!(oe<0||oe>=c.outHeight||Math.floor(oe)!==oe))for(let Y=0;Y<b;Y+=g){let re=(H+Y)/m;if(re<0||re>=c.outWidth||Math.floor(re)!==re)continue;q+=R.get(O,oe,re,M)}}k.set(q*D,O,G,j,M)}return t.makeTensorInfo(k.shape,k.dtype,k.values)}var pA={kernelName:dc,backendName:"cpu",kernelFunc:dK};function hK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,scale:s,offset:a,mean:i,variance:l}=e;y.assert(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||i.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(s==null||i.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),te([n,i,l,s,a],"batchNorm");let{varianceEpsilon:u}=o;u==null&&(u=.001);let c=t.data.get(n.dataId).values,p=t.data.get(i.dataId).values,m=t.data.get(l.dataId).values,f=s?t.data.get(s.dataId).values:new Float32Array([1]),d=a?t.data.get(a.dataId).values:new Float32Array([0]),h=new Float32Array(c.length),g=d.length,x=f.length,b=m.length,w=p.length,_=0,k=0,D=0,T=0;for(let R=0;R<c.length;++R)h[R]=d[_++]+(c[R]-p[k++])*f[D++]/Math.sqrt(m[T++]+u),_>=g&&(_=0),k>=w&&(k=0),D>=x&&(D=0),T>=b&&(T=0);return t.makeTensorInfo(n.shape,n.dtype,h)}var mA={kernelName:Pn,backendName:"cpu",kernelFunc:hK};function gK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;te([n],"batchToSpaceND");let i=s.reduce((x,b)=>x*b),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=Qe({inputs:{x:n},backend:t,attrs:{shape:l}}),d=or({inputs:{x:f},backend:t,attrs:{perm:u}}),h=Qe({inputs:{x:d},backend:t,attrs:{shape:c}}),g=un({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var fA={kernelName:ja,backendName:"cpu",kernelFunc:gK};function xK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,l=t.data.get(s.dataId).values,u=dp(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var dA={kernelName:gc,backendName:"cpu",kernelFunc:xK};var yK=$e(qo,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),hA={kernelName:qo,backendName:"cpu",kernelFunc:yK};var bK=r=>{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(y.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];o[u]=Math.hypot(c,p)}return t.makeOutput(o,e.shape,"float32")},gA={kernelName:Ua,backendName:"cpu",kernelFunc:bK};function ni(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.data.get(o.dataId).complexTensorInfos.imag,s=t.data.get(n.dataId).values;return t.makeTensorInfo(n.shape,n.dtype,s)}var xA={kernelName:Tc,backendName:"cpu",kernelFunc:ni};function ml(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(h=>h.shape),s);if(y.sizeFromShape(a)===0)return 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_A={kernelName:Tn,backendName:"cpu",kernelFunc:_K};function kK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o;te([n,s],"conv3d");let u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=u,x=g.front,b=g.left,w=g.top,_=new lt(u.outShape,n.dtype),k=t.data.get(n.dataId).values,D=t.data.get(s.dataId).values,T=_.values,R=y.computeStrides(n.shape),O=y.computeStrides(s.shape);for(let M=0;M<u.batchSize;++M){let G=M*R[0],j=M*_.strides[0];for(let U=0;U<u.outDepth;++U){let H=j+U*_.strides[1],q=U*u.strideDepth-x;for(let X=0;X<c;++X){let oe=q+X*f;if(oe<0||oe>=u.inDepth)continue;let Y=X*O[0],re=G+oe*R[1];for(let J=0;J<u.outHeight;++J){let ie=H+J*_.strides[2],ue=J*u.strideHeight-w;for(let ae=0;ae<p;++ae){let fe=ue+ae*d;if(fe<0||fe>=u.inHeight)continue;let de=Y+ae*O[1],xe=re+fe*R[2];for(let we=0;we<u.outWidth;++we){let 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u=y.computeStrides(n.shape),c=y.computeStrides(s.shape),p=N.computeConv3DInfo(l,s.shape,i,1,a),m=new lt(p.inShape,"float32"),f=m.values,[d,h,g,x]=m.strides,b=t.data.get(n.dataId).values,[w,_,k,D]=u,T=t.data.get(s.dataId).values,[R,O,M,G]=c,{batchSize:j,filterDepth:U,filterHeight:H,filterWidth:q,inChannels:X,inDepth:oe,inHeight:Y,inWidth:re,outChannels:J,outDepth:ie,outHeight:ue,outWidth:ae,strideDepth:fe,strideHeight:de,strideWidth:xe}=p,we=U-1-p.padInfo.front,De=H-1-p.padInfo.top,Ne=q-1-p.padInfo.left;for(let ze=0;ze<j;++ze)for(let qe=0;qe<X;++qe)for(let it=0;it<oe;++it){let St=it-we,Tt=Math.max(0,Math.ceil(St/fe)),Ue=Math.min(ie,(U+St)/fe);for(let ut=0;ut<Y;++ut){let mt=ut-De,Pt=Math.max(0,Math.ceil(mt/de)),_o=Math.min(ue,(H+mt)/de);for(let Xt=0;Xt<re;++Xt){let io=Xt-Ne,Or=Math.max(0,Math.ceil(io/xe)),jo=Math.min(ae,(q+io)/xe),nr=0;for(let ko=Tt;ko<Ue;++ko){let jr=ko*fe-St;for(let wr=Pt;wr<_o;++wr){let ao=wr*de-mt;for(let $o=Or;$o<jo;++$o){let 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SA={kernelName:Ni,backendName:"cpu",kernelFunc:SK};function TK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;te(n,"cumsum");let l=N.getAxesPermutation([s],n.shape.length),u=n;l!=null&&(u=or({inputs:{x:n},backend:t,attrs:{perm:l}}));let c=N.getInnerMostAxes(1,n.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let p=ir(u.dtype,"int32"),m=y.makeZerosTypedArray(y.sizeFromShape(u.shape),p),f=t.data.get(u.dataId).values,d=u.shape[u.shape.length-1],h=i?(x,b)=>x+d-b-1:(x,b)=>x+b;for(let x=0;x<f.length;x+=d)for(let b=0;b<d;b++){let w=h(x,b);if(b===0)m[w]=a?0:f[w];else{let _=h(x,b-1);m[w]=a?f[_]+m[_]:f[w]+m[_]}}let g=t.makeTensorInfo(u.shape,p,m);if(l!=null){let x=N.getUndoAxesPermutation(l),b=or({inputs:{x:g},backend:t,attrs:{perm:x}});return t.disposeIntermediateTensorInfo(g),t.disposeIntermediateTensorInfo(u),b}return g}var TA={kernelName:En,backendName:"cpu",kernelFunc:TK};function AK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.data.get(n.dataId).values,u=t.data.get(s.dataId).values,c=dp(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=Ag(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var AA={kernelName:_c,backendName:"cpu",kernelFunc:AK};function EK(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(a==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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y=C.expandShapeToKeepDim(p,i),b=Ze({inputs:{x:g},backend:t,attrs:{shape:y}});return t.disposeIntermediateTensorInfo(g),b}return g}var dA={kernelName:vi,backendName:"cpu",kernelFunc:TK};function AK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;te(o,"any");let i=x.parseAxisParam(s,o.shape),l=i,u=C.getAxesPermutation(l,o.shape.length),c=o;u!=null&&(c=Kt({inputs:{x:o},backend:t,attrs:{perm:u}}),l=C.getInnerMostAxes(l.length,o.shape.length)),C.assertAxesAreInnerMostDims("any",l,c.shape.length);let[p,m]=C.computeOutAndReduceShapes(c.shape,l),f=x.sizeFromShape(m),d=x.makeZerosTypedArray(x.sizeFromShape(p),c.dtype),h=t.data.get(c.dataId).values;for(let y=0;y<d.length;++y){let b=y*f,w=h[b];for(let _=0;_<f;++_){let I=h[b+_];w=w||I}d[y]=w}u!=null&&t.disposeIntermediateTensorInfo(c);let g=t.makeTensorInfo(p,c.dtype,d);if(a){let y=C.expandShapeToKeepDim(p,i),b=Ze({inputs:{x:g},backend:t,attrs:{shape:y}});return t.disposeIntermediateTensorInfo(g),b}return g}var hA={kernelName:Ci,backendName:"cpu",kernelFunc:AK};function EK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n;te(o,"argMax");let a=x.parseAxisParam(s,o.shape),i=C.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Kt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=C.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],C.assertAxesAreInnerMostDims("argMax",a,l.shape.length);let[c,p]=C.computeOutAndReduceShapes(l.shape,a),m=x.sizeFromShape(c),f=x.makeZerosTypedArray(m,"int32"),d=x.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;g<f.length;++g){let y=g*d,b=h[y],w=0;for(let _=0;_<d;++_){let I=h[y+_];I>b&&(b=I,w=_)}f[g]=w}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var gA={kernelName:_o,backendName:"cpu",kernelFunc:EK};function DK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n;te(o,"argMin");let a=x.parseAxisParam(s,o.shape),i=C.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=Kt({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=C.getInnerMostAxes(a.length,l.shape.length)),a=[a[0]],C.assertAxesAreInnerMostDims("argMin",a,l.shape.length);let[c,p]=C.computeOutAndReduceShapes(l.shape,a),m=x.sizeFromShape(c),f=x.makeZerosTypedArray(m,"int32"),d=x.sizeFromShape(p),h=t.data.get(l.dataId).values;for(let g=0;g<f.length;++g){let y=g*d,b=h[y],w=0;for(let _=0;_<d;++_){let I=h[y+_];I<b&&(b=I,w=_)}f[g]=w}return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),t.makeTensorInfo(c,"int32",f)}var xA={kernelName:Ka,backendName:"cpu",kernelFunc:DK};var $K=De(Ii,r=>Math.asin(r)),yA={kernelName:Ii,backendName:"cpu",kernelFunc:$K};var RK=De(Ni,r=>Math.asinh(r)),bA={kernelName:Ni,backendName:"cpu",kernelFunc:RK};var FK=De(Si,r=>Math.atan(r)),wA={kernelName:Si,backendName:"cpu",kernelFunc:FK};var 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c=C.computePool3DInfo(s.shape,a,i,1,l,u),p=c.strideDepth,m=c.strideHeight,f=c.strideWidth,d=c.filterDepth,h=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,b=c.dilationHeight,w=c.dilationWidth,_=c.effectiveFilterDepth,I=c.effectiveFilterHeight,E=c.effectiveFilterWidth,$=_-1-c.padInfo.front,D=E-1-c.padInfo.left,O=I-1-c.padInfo.top,M=Ce(s.shape,"float32"),G=1/(d*h*g),j=t.bufferSync(o);for(let U=0;U<c.batchSize;++U)for(let H=0;H<c.inChannels;++H)for(let q=0;q<c.inDepth;++q)for(let X=0;X<c.inHeight;++X)for(let ne=0;ne<c.inWidth;++ne){let Y=q-$,re=X-O,Q=ne-D,ie=0;for(let ce=0;ce<_;ce+=y){let ae=(Y+ce)/p;if(!(ae<0||ae>=c.outDepth||Math.floor(ae)!==ae))for(let fe=0;fe<I;fe+=b){let de=(re+fe)/m;if(!(de<0||de>=c.outHeight||Math.floor(de)!==de))for(let xe=0;xe<E;xe+=w){let we=(Q+xe)/f;if(we<0||we>=c.outWidth||Math.floor(we)!==we)continue;ie+=j.get(U,ae,de,we,H)}}}M.set(ie*G,U,q,X,ne,H)}return t.makeTensorInfo(M.shape,M.dtype,M.values)}var NA={kernelName:xc,backendName:"cpu",kernelFunc:BK};function VK(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;te([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=C.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,_=y-1-c.padInfo.top,I=Ce(a.shape,"float32"),E=1/(f*d),$=t.data.get(o.dataId).values,D=Ce(o.shape,"float32",$);for(let O=0;O<c.batchSize;++O)for(let M=0;M<c.inChannels;++M)for(let G=0;G<c.inHeight;++G)for(let j=0;j<c.inWidth;++j){let U=G-_,H=j-w,q=0;for(let X=0;X<y;X+=h){let ne=(U+X)/p;if(!(ne<0||ne>=c.outHeight||Math.floor(ne)!==ne))for(let Y=0;Y<b;Y+=g){let re=(H+Y)/m;if(re<0||re>=c.outWidth||Math.floor(re)!==re)continue;q+=D.get(O,ne,re,M)}}I.set(q*E,O,G,j,M)}return t.makeTensorInfo(I.shape,I.dtype,I.values)}var SA={kernelName:gc,backendName:"cpu",kernelFunc:VK};function 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t.makeTensorInfo(o.shape,o.dtype,h)}var TA={kernelName:Fo,backendName:"cpu",kernelFunc:GK};function WK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;te([o],"batchToSpaceND");let i=s.reduce((y,b)=>y*b),l=C.getReshaped(o.shape,s,i),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(o.shape,s,i),p=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),f=Ze({inputs:{x:o},backend:t,attrs:{shape:l}}),d=Kt({inputs:{x:f},backend:t,attrs:{perm:u}}),h=Ze({inputs:{x:d},backend:t,attrs:{shape:c}}),g=lo({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var AA={kernelName:Ya,backendName:"cpu",kernelFunc:WK};function jK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.data.get(o.dataId).values,l=t.data.get(s.dataId).values,u=xp(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var EA={kernelName:yc,backendName:"cpu",kernelFunc:jK};var UK=De(Kn,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),DA={kernelName:Kn,backendName:"cpu",kernelFunc:UK};var HK=r=>{let{x:e}=r.inputs,t=r.backend,n=new Float32Array(x.sizeFromShape(e.shape)),o=t.data.get(e.dataId),s=o.complexTensorInfos.real,a=o.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];n[u]=Math.hypot(c,p)}return t.makeOutput(n,e.shape,"float32")},$A={kernelName:Za,backendName:"cpu",kernelFunc:HK};function ai(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.data.get(n.dataId).complexTensorInfos.imag,s=t.data.get(o.dataId).values;return t.makeTensorInfo(o.shape,o.dtype,s)}var RA={kernelName:Dc,backendName:"cpu",kernelFunc:ai};function gl(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=x.parseAxisParam(o,e[0].shape)[0],a=C.computeOutShape(e.map(h=>h.shape),s);if(x.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(h=>x.sizeFromShape(h.shape)>0);if(i.length===1)return $r({inputs:{x:i[0]},backend:t});let l=i.map(h=>h.shape);if(C.assertParamsConsistent(l,s),i[0].dtype==="complex64"){let h=i.map(_=>oo({inputs:{input:_},backend:t})),g=i.map(_=>ai({inputs:{input:_},backend:t})),y=gl({inputs:h,backend:t,attrs:{axis:s}}),b=gl({inputs:g,backend:t,attrs:{axis:s}}),w=dr({inputs:{real:y,imag:b},backend:t});return h.forEach(_=>t.disposeIntermediateTensorInfo(_)),g.forEach(_=>t.disposeIntermediateTensorInfo(_)),t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(b),w}let u=i.map(h=>{let g=x.sizeFromShape(h.shape.slice(s));return Ze({inputs:{x:h},backend:t,attrs:{shape:[-1,g]}})}),c=u.map(h=>({vals:t.data.get(h.dataId).values,shape:h.shape}));a=C.computeOutShape(u.map(h=>h.shape),1);let p=u[0].shape[0]===1,m=Su(c,a,e[0].dtype,p),f=C.computeOutShape(i.map(h=>h.shape),s),d=t.makeTensorInfo(f,e[0].dtype,m);return u.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var FA={kernelName:zs,backendName:"cpu",kernelFunc:gl};function Y_(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n;te([o,s],"conv2d");let p=C.convertConv2DDataFormat(l),m=C.computeConv2DInfo(o.shape,s.shape,a,u,i,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,y=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",_=new ut(m.outShape,o.dtype),I=x.computeStrides(o.shape),E=x.computeStrides(s.shape),$=I[0],D=w?I[1]:I[2],O=w?I[2]:1,M=w?1:I[1],G=_.strides[0],j=w?_.strides[1]:_.strides[2],U=w?_.strides[2]:1,H=w?1:_.strides[1],q=t.data.get(o.dataId).values,X=t.data.get(s.dataId).values,ne=_.values;for(let Y=0;Y<m.batchSize;++Y){let re=Y*$,Q=Y*G;for(let ie=0;ie<m.outHeight;++ie){let ce=Q+ie*j,ae=ie*m.strideHeight-b;for(let fe=0;fe<f;++fe){let de=ae+fe*h;if(de<0||de>=m.inHeight)continue;let xe=fe*E[0],we=re+de*D;for(let Ee=0;Ee<m.outWidth;++Ee){let ve=ce+Ee*U,Ge=Ee*m.strideWidth-y;for(let Ke=0;Ke<d;++Ke){let at=Ge+Ke*g;if(at<0||at>=m.inWidth)continue;let St=xe+Ke*E[1],Tt=we+at*O,He=St;for(let ct=0;ct<m.inChannels;++ct){let mt=q[Tt+ct*M];for(let Lt=0;Lt<m.outChannels;++Lt)ne[ve+Lt*H]+=mt*X[He+Lt];He+=m.outChannels}}}}}}return t.makeTensorInfo(_.shape,_.dtype,ne)}var OA={kernelName:Io,backendName:"cpu",kernelFunc:Y_};function qK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n;te([o,s],"conv2dBackpropFilter");let p=C.convertConv2DDataFormat(l),m=C.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),{strideHeight:f,strideWidth:d,filterHeight:h,filterWidth:g}=m,y=m.dataFormat==="channelsLast",b=new ut(m.filterShape,"float32"),w=m.padInfo.left,_=m.padInfo.top,I=t.data.get(o.dataId).values,E=t.data.get(s.dataId).values,$=new ut(o.shape,o.dtype,I),D=new ut(s.shape,s.dtype,E);for(let O=0;O<h;++O){let M=Math.max(0,Math.ceil((_-O)/f)),G=Math.min(m.outHeight,(m.inHeight+_-O)/f);for(let j=0;j<g;++j){let U=Math.max(0,Math.ceil((w-j)/d)),H=Math.min(m.outWidth,(m.inWidth+w-j)/d);for(let q=0;q<m.inChannels;++q)for(let X=0;X<m.outChannels;++X){let ne=0;for(let Y=0;Y<m.batchSize;++Y)for(let re=M;re<G;++re){let Q=O+re*f-_;for(let ie=U;ie<H;++ie){let ce=j+ie*d-w;y?ne+=$.get(Y,Q,ce,q)*D.get(Y,re,ie,X):ne+=$.get(Y,q,Q,ce)*D.get(Y,X,re,ie)}}b.set(ne,O,j,q,X)}}}return t.makeTensorInfo(b.shape,b.dtype,b.values)}var PA={kernelName:wc,backendName:"cpu",kernelFunc:qK};function KK(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n;te([o,s],"conv2dBackpropInput");let p=x.computeStrides(s.shape),m=x.computeStrides(o.shape),f=C.convertConv2DDataFormat(u),d=C.computeConv2DInfo(a,s.shape,i,1,l,c,!1,f),h=new 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MA={kernelName:No,backendName:"cpu",kernelFunc:KK};function XK(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n;te([o,s],"conv3d");let u=C.computeConv3DInfo(o.shape,s.shape,a,l,i),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=u,y=g.front,b=g.left,w=g.top,_=new ut(u.outShape,o.dtype),I=t.data.get(o.dataId).values,E=t.data.get(s.dataId).values,$=_.values,D=x.computeStrides(o.shape),O=x.computeStrides(s.shape);for(let M=0;M<u.batchSize;++M){let G=M*D[0],j=M*_.strides[0];for(let U=0;U<u.outDepth;++U){let H=j+U*_.strides[1],q=U*u.strideDepth-y;for(let X=0;X<c;++X){let ne=q+X*f;if(ne<0||ne>=u.inDepth)continue;let Y=X*O[0],re=G+ne*D[1];for(let Q=0;Q<u.outHeight;++Q){let ie=H+Q*_.strides[2],ce=Q*u.strideHeight-w;for(let ae=0;ae<p;++ae){let fe=ce+ae*d;if(fe<0||fe>=u.inHeight)continue;let de=Y+ae*O[1],xe=re+fe*D[2];for(let we=0;we<u.outWidth;++we){let 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ce=Math.max(0,Math.ceil((Y-ie)/m)),ae=Math.min(p.outDepth,(p.inDepth+Y-ie)/m),fe=ie*_;for(let de=0;de<g;++de){let xe=Math.max(0,Math.ceil((Q-de)/f)),we=Math.min(p.outHeight,(p.inHeight+Q-de)/f),Ee=de*I+fe;for(let ve=0;ve<y;++ve){let Ge=Math.max(0,Math.ceil((re-ve)/d)),Ke=Math.min(p.outWidth,(p.inWidth+re-ve)/d),at=ve*E+Ee;for(let St=0;St<p.inChannels;++St){let Tt=St*$+at;for(let He=0;He<p.outChannels;++He){let ct=0;for(let mt=0;mt<p.batchSize;++mt){let Lt=mt*H,kn=mt*O;for(let Yt=ce;Yt<ae;++Yt){let Fr=(ie+Yt*m-Y)*q+Lt,Un=Yt*M+kn;for(let sr=xe;sr<we;++sr){let jr=(de+sr*f-Q)*X+Fr,wr=sr*G+Un;for(let cn=Ge;cn<Ke;++cn){let Ml=(ve+cn*d-re)*ne+jr,Va=cn*j+wr;ct+=U[Ml+St]*D[Va+He]}}}}w[Tt+He]=ct}}}}}return t.makeTensorInfo(b.shape,b.dtype,b.values)}var zA={kernelName:_c,backendName:"cpu",kernelFunc:YK};function ZK(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n;te([o],"conv3dBackpropInputV2");let u=x.computeStrides(o.shape),c=x.computeStrides(s.shape),p=C.computeConv3DInfo(l,s.shape,i,1,a),m=new ut(p.inShape,"float32"),f=m.values,[d,h,g,y]=m.strides,b=t.data.get(o.dataId).values,[w,_,I,E]=u,$=t.data.get(s.dataId).values,[D,O,M,G]=c,{batchSize:j,filterDepth:U,filterHeight:H,filterWidth:q,inChannels:X,inDepth:ne,inHeight:Y,inWidth:re,outChannels:Q,outDepth:ie,outHeight:ce,outWidth:ae,strideDepth:fe,strideHeight:de,strideWidth:xe}=p,we=U-1-p.padInfo.front,Ee=H-1-p.padInfo.top,ve=q-1-p.padInfo.left;for(let Ge=0;Ge<j;++Ge)for(let Ke=0;Ke<X;++Ke)for(let at=0;at<ne;++at){let St=at-we,Tt=Math.max(0,Math.ceil(St/fe)),He=Math.min(ie,(U+St)/fe);for(let ct=0;ct<Y;++ct){let mt=ct-Ee,Lt=Math.max(0,Math.ceil(mt/de)),kn=Math.min(ce,(H+mt)/de);for(let Yt=0;Yt<re;++Yt){let un=Yt-ve,Fr=Math.max(0,Math.ceil(un/xe)),Un=Math.min(ae,(q+un)/xe),sr=0;for(let vn=Tt;vn<He;++vn){let jr=vn*fe-St;for(let wr=Lt;wr<kn;++wr){let cn=wr*de-mt;for(let Rn=Fr;Rn<Un;++Rn){let Ml=Rn*xe-un,Va=w*Ge+_*vn+I*wr+E*Rn,Fs=D*(U-1-jr)+O*(H-1-cn)+M*(q-1-Ml)+G*Ke;for(let gi=0;gi<Q;++gi){let am=b[Va+gi],Ll=$[Fs+gi];sr+=am*Ll}}}}f[d*Ge+h*at+g*ct+y*Yt+Ke]=sr}}}return t.makeTensorInfo(m.shape,m.dtype,m.values)}var BA={kernelName:kc,backendName:"cpu",kernelFunc:ZK};var JK=De(So,r=>Math.cos(r)),VA={kernelName:So,backendName:"cpu",kernelFunc:JK};var QK=De(Ei,r=>Math.cosh(r)),GA={kernelName:Ei,backendName:"cpu",kernelFunc:QK};function e6(r){let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,[c,p,m,f]=o.shape,d=s.shape[0],[h,g]=i,y=Ce([d,h,g,f],"float32"),b=t.data.get(s.dataId).values,w=t.data.get(a.dataId).values,_=t.data.get(o.dataId).values,I=x.computeStrides(o.shape),E=x.computeStrides(y.shape);for(let $=0;$<d;$++){let D=$*4,O=b[D],M=b[D+1],G=b[D+2],j=b[D+3],U=w[$];if(U>=c)continue;let H=h>1?(G-O)*(p-1)/(h-1):0,q=g>1?(j-M)*(m-1)/(g-1):0;for(let X=0;X<h;X++){let ne=h>1?O*(p-1)+X*H:.5*(O+G)*(p-1);if(ne<0||ne>p-1){for(let 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WA={kernelName:Di,backendName:"cpu",kernelFunc:e6};function t6(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n;te(o,"cumsum");let l=C.getAxesPermutation([s],o.shape.length),u=o;l!=null&&(u=Kt({inputs:{x:o},backend:t,attrs:{perm:l}}));let c=C.getInnerMostAxes(1,o.shape.length)[0];if(c!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${c}`);let p=ar(u.dtype,"int32"),m=x.makeZerosTypedArray(x.sizeFromShape(u.shape),p),f=t.data.get(u.dataId).values,d=u.shape[u.shape.length-1],h=i?(y,b)=>y+d-b-1:(y,b)=>y+b;for(let y=0;y<f.length;y+=d)for(let b=0;b<d;b++){let w=h(y,b);if(b===0)m[w]=a?0:f[w];else{let _=h(y,b-1);m[w]=a?f[_]+m[_]:f[w]+m[_]}}let g=t.makeTensorInfo(u.shape,p,m);if(l!=null){let y=C.getUndoAxesPermutation(l),b=Kt({inputs:{x:g},backend:t,attrs:{perm:y}});return t.disposeIntermediateTensorInfo(g),t.disposeIntermediateTensorInfo(u),b}return g}var jA={kernelName:To,backendName:"cpu",kernelFunc:t6};function r6(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.data.get(o.dataId).values,u=t.data.get(s.dataId).values,c=xp(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=Rg(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var UA={kernelName:vc,backendName:"cpu",kernelFunc:r6};function n6(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;x.assert(a==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Pe=W();Pe.registerFlag("HAS_WEBGL",()=>Pe.getNumber("WEBGL_VERSION")>0);Pe.registerFlag("WEBGL_VERSION",()=>Kg(2)?2:Kg(1)?1:0);Pe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Pe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Pe.get("WEBGL_VERSION")===2);Pe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Pe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Pe.registerFlag("WEBGL_PACK",()=>Pe.getBool("HAS_WEBGL"));Pe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_CLIP",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Pe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_PACK_REDUCE",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_CONV_IM2COL",()=>Pe.getBool("WEBGL_PACK"));Pe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>sk(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>ik(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Pe.getNumber("WEBGL_VERSION");return r===0?0:ak(r)});Pe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Pe.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!jl.isMobile());Pe.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>uk(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Pe.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Pe.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Pe.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>ck(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_FENCE_API_ENABLED",()=>pk(Pe.getNumber("WEBGL_VERSION")));Pe.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Pe.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Pe.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Pe.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>jl.isMobile()&&Pe.getBool("IS_CHROME")?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});function Rt(){let r,e,t,o,n,s,a,i,l,u;return W().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",e="in",t="out",o="in",n="texture",s="outputColor",a="out vec4 outputColor;",i=`
+`)[0]),console.log(`%c ${x.rightPad(u[0],i)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
+`))}function lk(r){return Aa(r,()=>r.createProgram(),"Unable to create WebGLProgram.")}function uk(r,e){if(Ne(r,()=>r.linkProgram(e)),r.getProgramParameter(e,r.LINK_STATUS)===!1)throw console.log(r.getProgramInfoLog(e)),new Error("Failed to link vertex and fragment shaders.")}function Tf(r,e){if(Ne(r,()=>r.validateProgram(e)),r.getProgramParameter(e,r.VALIDATE_STATUS)===!1)throw console.log(r.getProgramInfoLog(e)),new Error("Shader program validation failed.")}function ck(r,e){let t=Aa(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return Ne(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Ne(r,()=>r.bufferData(r.ARRAY_BUFFER,e,r.STATIC_DRAW)),t}function pk(r,e){let t=Aa(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return Ne(r,()=>r.bindBuffer(r.ELEMENT_ARRAY_BUFFER,t)),Ne(r,()=>r.bufferData(r.ELEMENT_ARRAY_BUFFER,e,r.STATIC_DRAW)),t}function s8(){return W().getNumber("WEBGL_VERSION")===2?1:4}function mk(r){return Aa(r,()=>r.createTexture(),"Unable to create WebGLTexture.")}function fk(r,e){let t=W().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(r<=0||e<=0){let n=`[${r}x${e}]`;throw new Error("Requested texture size "+n+" is invalid.")}if(r>t||e>t){let n=`[${r}x${e}]`,o=`[${t}x${t}]`;throw new Error("Requested texture size "+n+" greater than WebGL maximum on this browser / GPU "+o+".")}}function dk(r){return Aa(r,()=>r.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function qg(r,e,t,n,o,s,a){let i=r.getAttribLocation(e,t);return i===-1?!1:(Ne(r,()=>r.bindBuffer(r.ARRAY_BUFFER,n)),Ne(r,()=>r.vertexAttribPointer(i,o,r.FLOAT,!1,s,a)),Ne(r,()=>r.enableVertexAttribArray(i)),!0)}function O2(r,e,t){F2(r,t),Ne(r,()=>r.activeTexture(r.TEXTURE0+t)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,e))}function i8(r,e){F2(r,e),Ne(r,()=>r.activeTexture(r.TEXTURE0+e)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function hk(r,e,t){return Aa(r,()=>r.getUniformLocation(e,t),'uniform "'+t+'" not present in program.')}function gk(r,e,t){return r.getUniformLocation(e,t)}function xk(r,e,t,n){Ne(r,()=>O2(r,e,n)),Ne(r,()=>r.uniform1i(t,n))}function a8(r){Ne(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,null)),Ne(r,()=>r.viewport(0,0,r.canvas.width,r.canvas.height)),Ne(r,()=>r.scissor(0,0,r.canvas.width,r.canvas.height))}function Af(r,e,t){Ne(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,t)),Ne(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e,0))}function Kg(r,e){Ne(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,e)),Ne(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,null,0))}function vp(r){let e=r.checkFramebufferStatus(r.FRAMEBUFFER);if(e!==r.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+P2(r,e))}function P2(r,e){switch(e){case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case r.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case r.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case r.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${e}`}}function Aa(r,e,t){let n=Ne(r,()=>e());if(n==null)throw new Error(t);return n}function F2(r,e){let t=r.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,n=e+r.TEXTURE0;if(n<r.TEXTURE0||n>t){let o=`[gl.TEXTURE0, gl.TEXTURE${t}]`;throw new Error(`textureUnit must be in ${o}.`)}}function Ea(r,e=2){return x.sizeFromShape(r.slice(0,r.length-e))}function Da(r){if(r.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[r.length>1?r[r.length-2]:1,r[r.length-1]]}function Ef(r){let e=[1,1,1];return r.length===0||r.length===1&&r[0]===1||(e=[Ea(r),...Da(r)]),e}function yk(r,e=!1){let t=W().getNumber("WEBGL_MAX_TEXTURE_SIZE");e&&(t=t*2,r=r.map((o,s)=>s>=r.length-2?x.nearestLargerEven(r[s]):r[s]),r.length===1&&(r=[2,r[0]])),r.length!==2&&(r=x.squeezeShape(r).newShape);let n=x.sizeFromShape(r);if(r.length<=1&&n<=t)return[1,n];if(r.length===2&&r[0]<=t&&r[1]<=t)return r;if(r.length===3&&r[0]*r[1]<=t&&r[2]<=t)return[r[0]*r[1],r[2]];if(r.length===3&&r[0]<=t&&r[1]*r[2]<=t)return[r[0],r[1]*r[2]];if(r.length===4&&r[0]*r[1]*r[2]<=t&&r[3]<=t)return[r[0]*r[1]*r[2],r[3]];if(r.length===4&&r[0]<=t&&r[1]*r[2]*r[3]<=t)return[r[0],r[1]*r[2]*r[3]];if(e){let o=Ea(r),s=2,a=2;return r.length&&([s,a]=Da(r)),n=o*(s/2)*(a/2),x.sizeToSquarishShape(n).map(i=>i*2)}return x.sizeToSquarishShape(n)}function Xg(r){return r%2==0}function bl(r,e){if(r=r.slice(-2),e=e.slice(-2),x.arraysEqual(r,e)||!r.length||!e.length||r[0]===0||r[1]===0||e[0]===0||e[1]===0)return!0;if(r.length!==e.length){let t=r.slice(-1)[0],n=e.slice(-1)[0];if(t===n||Xg(t)&&Xg(n)&&(r[0]===1||e[0]===1))return!0}return r[1]===e[1]&&Xg(r[0])&&Xg(e[0])}var Yg,Zg;function bk(r){if(Yg==null){let e=Vn(r);Yg=e.getParameter(e.MAX_TEXTURE_SIZE)}return Yg}function l8(){Yg=null}function u8(){Zg=null}function wk(r){if(Zg==null){let e=Vn(r);Zg=e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Zg)}function _k(r){if(r===0)return 0;let e,t=Vn(r);return Tn(t,"EXT_disjoint_timer_query_webgl2")&&r===2?e=2:Tn(t,"EXT_disjoint_timer_query")?e=1:e=0,e}function Tn(r,e){return r.getExtension(e)!=null}function Jg(r){try{if(Vn(r)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function vk(r){if(r===0)return!1;let e=Vn(r);if(r===1){if(!Tn(e,"OES_texture_float"))return!1}else if(!Tn(e,"EXT_color_buffer_float"))return!1;return kk(e)}function Ck(r){if(r===0)return!1;let e=Vn(r);if(r===1){if(!Tn(e,"OES_texture_float")||!Tn(e,"WEBGL_color_buffer_float"))return!1}else{if(Tn(e,"EXT_color_buffer_float"))return kk(e);let n="EXT_color_buffer_half_float";if(Tn(e,n)){let o=e.getExtension(n);return c8(e,o)}return!1}return kk(e)}function kk(r){let e=Sf(r),t=r.createTexture();r.bindTexture(r.TEXTURE_2D,t);let n=1,o=1;r.texImage2D(r.TEXTURE_2D,0,e.internalFormatFloat,n,o,0,e.textureFormatFloat,e.textureTypeFloat,null);let s=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,s),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,t,0);let a=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(t),r.deleteFramebuffer(s),a}function c8(r,e){let t=Sf(r,e),n=r.createTexture();r.bindTexture(r.TEXTURE_2D,n);let o=1,s=1;r.texImage2D(r.TEXTURE_2D,0,t.internalFormatHalfFloat,o,s,0,t.textureFormatFloat,t.textureTypeHalfFloat,null);let a=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,a),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,n,0);let i=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(n),r.deleteFramebuffer(a),i}function Ik(r){return r!==2?!1:Vn(r).fenceSync!=null}function ks(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&x.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the WebGL backend.`)})}var Me=W();Me.registerFlag("HAS_WEBGL",()=>Me.getNumber("WEBGL_VERSION")>0);Me.registerFlag("WEBGL_VERSION",()=>Jg(2)?2:Jg(1)?1:0);Me.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Me.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Me.get("WEBGL_VERSION")===2);Me.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Me.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Me.registerFlag("WEBGL_PACK",()=>Me.getBool("HAS_WEBGL"));Me.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_CLIP",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!0);Me.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_REDUCE",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_LAZILY_UNPACK",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_CONV_IM2COL",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>bk(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>wk(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Me.getNumber("WEBGL_VERSION");return r===0?0:_k(r)});Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Me.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Yl.isMobile());Me.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>vk(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Me.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Me.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Me.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>Ck(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Ik(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Me.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Me.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Me.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Yl.isMobile()&&Me.getBool("IS_CHROME")?1:-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${r}.`)});function Ot(){let r,e,t,n,o,s,a,i,l,u;return W().getNumber("WEBGL_VERSION")===2?(r="#version 300 es",e="in",t="out",n="in",o="texture",s="outputColor",a="out vec4 outputColor;",i=`
       bool isnan_custom(float val) {
         return (val > 0.0 || val < 0.0) ? false : val != 0.0;
       }
@@ -49,7 +49,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 newRound(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `):(r="",e="attribute",t="varying",o="varying",n="texture2D",s="gl_FragColor",a="",i=`
+    `):(r="",e="attribute",t="varying",n="varying",o="texture2D",s="gl_FragColor",a="",i=`
       #define isnan(value) isnan_custom(value)
       bool isnan_custom(float val) {
         return (val > 0. || val < 1. || val == 0.) ? false : true;
@@ -74,11 +74,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function ks(r,e,t="index"){let o=y.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function _p(r){let e=y.computeStrides(r).map(t=>t.toString());return`
+    `),{version:r,attribute:e,varyingVs:t,varyingFs:n,texture2D:o,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function vs(r,e,t="index"){let n=x.computeStrides(e);return n.map((o,s)=>{let a=`int ${r[s]} = ${t} / ${o}`,i=s===n.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${o}`:`index -= ${r[s]} * ${o}`;return`${a}; ${i};`}).join("")}function Cp(r){let e=x.computeStrides(r).map(t=>t.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
   }
-`}var Xg=`
+`}var Qg=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -117,9 +117,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`;var mk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=fl.DENSE;let t=dl(e),o=Rt();this.outputShape=e,this.userCode=`
+`;var Nk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=xl.DENSE;let t=yl(e),n=Ot();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${ks(["r","c","d"],e)}
+        ${vs(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -136,11 +136,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
           result[i] = getA(rc.x, rc.y, rc.z);
         }
 
-        ${o.output} = result;
+        ${n.output} = result;
       }
-    `}};var fk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=fl.DENSE;let t=dl(e),o=Rt();this.outputShape=e,this.userCode=`
+    `}};var Sk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xl.DENSE;let t=yl(e),n=Ot();this.outputShape=e,this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${ks(["r","c","d"],e)}
+        ${vs(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -157,25 +157,25 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
           result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
         }
 
-        ${o.output} = result;
+        ${n.output} = result;
       }
-    `}};var dk=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Fr.DOWNLOAD;let t=Rt();this.outputShape=e,this.userCode=`
-      ${Xg}
+    `}};var Tk=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Rr.DOWNLOAD;let t=Ot();this.outputShape=e,this.userCode=`
+      ${Qg}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}};var hk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Fr.DOWNLOAD;let t=Rt();this.outputShape=e,this.userCode=`
-      ${Xg}
+    `}};var Ak=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Rr.DOWNLOAD;let t=Ot();this.outputShape=e,this.userCode=`
+      ${Qg}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}};var gk=class{constructor(e,t,o=!1){this.variableNames=["A"];let n=Rt(),[s,a]=t;this.outputShape=e;let i="result";o&&(i="floor(result * 255. + 0.5)"),this.userCode=`
-      ${_p(e)}
+    `}};var Ek=class{constructor(e,t,n=!1){this.variableNames=["A"];let o=Ot(),[s,a]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
+      ${Cp(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -188,7 +188,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
         int r = flatIndex / ${a};
         int c = imod(flatIndex, ${a});
         vec2 uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0);
-        vec4 values = ${n.texture2D}(A, uv);
+        vec4 values = ${o.texture2D}(A, uv);
 
         float result;
 
@@ -202,9 +202,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
           result = values[3];
         }
 
-        ${n.output} = vec4(${i}, 0., 0., 0.);
+        ${o.output} = vec4(${i}, 0., 0., 0.);
       }
-    `}};var xk=class{constructor(e,t,o=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let n=Rt(),[s,a]=t;this.outputShape=e;let i="",l="result";o&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
+    `}};var Dk=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let o=Ot(),[s,a]=t;this.outputShape=e;let i="",l="result";n&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
           localCoords = coords;
           if(localCoords[2] + ${c} < ${e[2]}) {
             localCoords[2] += ${c};
@@ -219,7 +219,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
               r = flatIndex / ${a};
               c = imod(flatIndex, ${a});
               uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0);
-              values = ${n.texture2D}(A, uv);
+              values = ${o.texture2D}(A, uv);
 
               if(offset == 0) {
                 result[${p}] = values[0];
@@ -233,7 +233,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
             }
           }
         `}this.userCode=`
-      ${_p(e)}
+      ${Cp(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -246,9 +246,9 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
         ${i}
 
-        ${n.output} = ${l};
+        ${o.output} = ${l};
       }
-    `}};var w2={};Ge(w2,{bindVertexProgramAttributeStreams:()=>Nk,createBufferFromOutputTexture:()=>Ak,createFloat16MatrixTexture:()=>kk,createFloat16PackedMatrixTexture:()=>Ik,createFloat32MatrixTexture:()=>_k,createIndexBuffer:()=>wk,createPackedMatrixTexture:()=>Ck,createUnsignedBytesMatrixTexture:()=>vk,createVertexBuffer:()=>bk,createVertexShader:()=>yk,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Dk,downloadFloat32MatrixFromBuffer:()=>Ek,downloadMatrixFromPackedOutputTexture:()=>Rk,downloadPackedMatrixFromBuffer:()=>$k,getInternalFormatForFloat16MatrixTexture:()=>Zg,getInternalFormatForFloat16PackedMatrixTexture:()=>ex,getInternalFormatForFloat32MatrixTexture:()=>Yg,getInternalFormatForPackedMatrixTexture:()=>Qg,getInternalFormatForUnsignedBytesMatrixTexture:()=>Jg,uploadDenseMatrixToTexture:()=>Sk,uploadPixelDataToTexture:()=>Tk});function yk(r){let e=Rt(),t=`${e.version}
+    `}};var M2={};We(M2,{bindVertexProgramAttributeStreams:()=>Bk,createBufferFromOutputTexture:()=>Wk,createFloat16MatrixTexture:()=>Pk,createFloat16PackedMatrixTexture:()=>zk,createFloat32MatrixTexture:()=>Ok,createIndexBuffer:()=>Fk,createPackedMatrixTexture:()=>Lk,createUnsignedBytesMatrixTexture:()=>Mk,createVertexBuffer:()=>Rk,createVertexShader:()=>$k,downloadByteEncodedFloatMatrixFromOutputTexture:()=>Uk,downloadFloat32MatrixFromBuffer:()=>jk,downloadMatrixFromPackedOutputTexture:()=>qk,downloadPackedMatrixFromBuffer:()=>Hk,getInternalFormatForFloat16MatrixTexture:()=>tx,getInternalFormatForFloat16PackedMatrixTexture:()=>ox,getInternalFormatForFloat32MatrixTexture:()=>ex,getInternalFormatForPackedMatrixTexture:()=>nx,getInternalFormatForUnsignedBytesMatrixTexture:()=>rx,uploadDenseMatrixToTexture:()=>Vk,uploadPixelDataToTexture:()=>Gk});function $k(r){let e=Ot(),t=`${e.version}
     precision highp float;
     ${e.attribute} vec3 clipSpacePos;
     ${e.attribute} vec2 uv;
@@ -257,22 +257,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return H_(r,t)}function bk(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Y_(r,e)}function wk(r){let e=new Uint16Array([0,1,2,2,1,3]);return Z_(r,e)}function Sf(r,e,t,o,n,s){Q_(e,t);let a=J_(r),i=r.TEXTURE_2D;return Ie(r,()=>r.bindTexture(i,a)),Ie(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ie(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ie(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ie(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ie(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)),Ie(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function Yg(r){return r.internalFormatFloat}function _k(r,e,t,o){let[n,s]=Nu(e,t);return Sf(r,n,s,Yg(o),o.textureFormatFloat,r.FLOAT)}function Zg(r){return r.internalFormatHalfFloat}function kk(r,e,t,o){let[n,s]=Nu(e,t);return Sf(r,n,s,Zg(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function Jg(r){return r.downloadTextureFormat}function vk(r,e,t,o){let[n,s]=Nu(e,t);return Sf(r,n,s,Jg(o),r.RGBA,r.UNSIGNED_BYTE)}function Qg(r){return r.internalFormatPackedFloat}function Ck(r,e,t,o){let[n,s]=si(e,t);return Sf(r,n,s,Qg(o),r.RGBA,r.FLOAT)}function ex(r){return r.internalFormatPackedHalfFloat}function Ik(r,e,t,o){let[n,s]=si(e,t);return Sf(r,n,s,ex(o),r.RGBA,o.textureTypeHalfFloat)}function Nk(r,e,t){let o=0,n=3*4,s=3*4+2*4;return Ie(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Wg(r,e,"clipSpacePos",t,3,s,o)&&Wg(r,e,"uv",t,2,s,n)}function Sk(r,e,t,o,n,s){Ie(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(n),Ie(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,o,0,r.RGBA,i,a)),Ie(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Tk(r,e,t){Ie(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ie(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ie(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ie(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Ak(r,e,t,o){let n=r.createBuffer();Ie(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return Ie(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ie(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ie(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function Ek(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function Dk(r,e,t,o){let[n,s]=Nu(e,t),a=4,i=new Uint8Array(f2(e*t,a));return Ie(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function $k(r,e,t,o,n,s,a,i){let l=r,u=new Float32Array(d2(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function Rk(r,e,t){let o=new Float32Array(e*t*4);return Ie(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var tx=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=W().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,j_(t,e)):this.gl=Bo(t);let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(W().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=bp(this.gl,s),So(this.gl,a))this.textureHalfFloatExtension=bp(this.gl,a);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(o),So(this.gl,n))this.colorBufferHalfFloatExtension=bp(this.gl,n);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(o="EXT_color_buffer_float",So(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(So(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=bk(this.gl),this.indexBuffer=wk(this.gl),this.framebuffer=ek(this.gl),this.textureConfig=vf(this.gl,this.textureHalfFloatExtension)}get debug(){return W().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=bp(this.gl,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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n=this.gl;If(n,e,this.framebuffer),this.debug&&wp(n),this.outputTexture=e,Ie(n,()=>n.viewport(0,0,t,o)),Ie(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function M5(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:_2}=N;function k2(r,e,t,o){let n=[];r.forEach(d=>{let h=y.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`))});let s=n.join(`
-`),a=r.map(d=>L5(d,e,o)).join(`
-`),i=e.texShape,l=Rt(),u=V5(l),c,p,m=j5(l);return e.isPacked?(c=z5(e.logicalShape,i),p=W5(l)):(c=B5(e.logicalShape,i),p=G5(l)),o&&(m+=U5),[m,u,p,s,c,a,t].join(`
-`)}function kp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return H5(r);case 1:return q5(r);case 2:return K5(r);case 3:return X5(r);case 4:return Y5(r);case 5:return Z5(r);case 6:return J5(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function v2(r){switch(r.shapeInfo.logicalShape.length){case 0:return Q5(r);case 1:return eX(r);case 2:return tX(r);case 3:return rX(r);default:return oX(r)}}function L5(r,e,t=!1){let o="";t?o+=v2(r):o+=kp(r);let n=r.shapeInfo.logicalShape,s=e.logicalShape;return n.length<=s.length&&(t?o+=nX(r,e):o+=sX(r,e)),o}function z5(r,e){switch(r.length){case 0:return C2();case 1:return iX(r,e);case 2:return uX(r,e);case 3:return aX(r,e);default:return lX(r,e)}}function B5(r,e){switch(r.length){case 0:return C2();case 1:return cX(r,e);case 2:return hX(r,e);case 3:return pX(r,e);case 4:return mX(r,e);case 5:return fX(r,e);case 6:return dX(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function V5(r){return`
+    }`;return ik(r,t)}function Rk(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return ck(r,e)}function Fk(r){let e=new Uint16Array([0,1,2,2,1,3]);return pk(r,e)}function Df(r,e,t,n,o,s){fk(e,t);let a=mk(r),i=r.TEXTURE_2D;return Ne(r,()=>r.bindTexture(i,a)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ne(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ne(r,()=>r.texImage2D(i,0,n,e,t,0,o,s,null)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function ex(r){return r.internalFormatFloat}function Ok(r,e,t,n){let[o,s]=$u(e,t);return Df(r,o,s,ex(n),n.textureFormatFloat,r.FLOAT)}function tx(r){return r.internalFormatHalfFloat}function Pk(r,e,t,n){let[o,s]=$u(e,t);return Df(r,o,s,tx(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function rx(r){return r.downloadTextureFormat}function Mk(r,e,t,n){let[o,s]=$u(e,t);return Df(r,o,s,rx(n),r.RGBA,r.UNSIGNED_BYTE)}function nx(r){return r.internalFormatPackedFloat}function Lk(r,e,t,n){let[o,s]=li(e,t);return Df(r,o,s,nx(n),r.RGBA,r.FLOAT)}function ox(r){return r.internalFormatPackedHalfFloat}function zk(r,e,t,n){let[o,s]=li(e,t);return Df(r,o,s,ox(n),r.RGBA,n.textureTypeHalfFloat)}function Bk(r,e,t){let n=0,o=3*4,s=3*4+2*4;return Ne(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),qg(r,e,"clipSpacePos",t,3,s,n)&&qg(r,e,"uv",t,2,s,o)}function Vk(r,e,t,n,o,s){Ne(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;o instanceof Uint8Array?(a=new Uint8Array(t*n*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*n*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(o),Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,n,0,r.RGBA,i,a)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Gk(r,e,t){Ne(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ne(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ne(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function Wk(r,e,t,n){let o=r.createBuffer();Ne(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,o));let i=4*4*e*t;return Ne(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ne(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ne(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),o}function jk(r,e,t){let n=r,o=new Float32Array(t);return n.bindBuffer(n.PIXEL_PACK_BUFFER,e),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,o),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),o}function Uk(r,e,t,n){let[o,s]=$u(e,t),a=4,i=new Uint8Array(E2(e*t,a));return Ne(r,()=>r.readPixels(0,0,o,s,n.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function Hk(r,e,t,n,o,s,a,i){let l=r,u=new Float32Array(D2(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function qk(r,e,t){let n=new Float32Array(e*t*4);return Ne(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,n)),n}var sx=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=W().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,ok(t,e)):this.gl=Vn(t);let n="WEBGL_color_buffer_float",o="EXT_color_buffer_half_float";if(W().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=kp(this.gl,s),Tn(this.gl,a))this.textureHalfFloatExtension=kp(this.gl,a);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Tn(this.gl,o))this.colorBufferHalfFloatExtension=kp(this.gl,o);else if(W().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Tn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Tn(this.gl,o))this.colorBufferHalfFloatExtension=this.gl.getExtension(o);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Rk(this.gl),this.indexBuffer=Fk(this.gl),this.framebuffer=dk(this.gl),this.textureConfig=Sf(this.gl,this.textureHalfFloatExtension)}get debug(){return W().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Ne(e,()=>e.finish()),Ne(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ne(e,()=>e.deleteFramebuffer(this.framebuffer)),Ne(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ne(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ne(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Ok(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Pk(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Mk(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),Gk(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,o){this.throwIfDisposed(),Vk(this.gl,e,t,n,o,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),zk(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Lk(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Kg(this.gl,this.framebuffer),this.outputTexture=null),Ne(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>Uk(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,o,s,a){return Hk(this.gl,e,t,n,o,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return jk(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let o=Wk(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),o}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(W().getBool("WEBGL_FENCE_API_ENABLED")){let o=e,s=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=o.clientWaitSync(s,0,0);return a===o.ALREADY_SIGNALED||a===o.CONDITION_SATISFIED},t=s}else W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>qk(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=ak(t,e),o=$k(t),s=lk(t);return Ne(t,()=>t.attachShader(s,o)),Ne(t,()=>t.attachShader(s,n)),uk(t,s),this.debug&&Tf(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=Bk(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ne(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Tf(this.gl,this.program),Ne(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?hk(this.gl,e,t):gk(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ne(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),xk(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[o,s]=li(t,n);this.setOutputMatrixTextureDriver(e,o,s)}setOutputMatrixWriteRegion(e,t,n,o){this.setOutputMatrixWriteRegionDriver(n,e,o,t)}setOutputPackedMatrixWriteRegion(e,t,n,o){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Tf(this.gl,this.program),vp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ne(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=kp(this.gl,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.createQuery();return n.beginQuery(o.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await x.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,o=this.getQueryTimerExtensionWebGL2(),s=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),o=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),o&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=p8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&x.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Af(this.gl,e,this.framebuffer),this.debug&&vp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Af(this.gl,this.outputTexture,this.framebuffer),this.debug&&vp(this.gl)):Kg(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let o=this.gl;Af(o,e,this.framebuffer),this.debug&&vp(o),this.outputTexture=e,Ne(o,()=>o.viewport(0,0,t,n)),Ne(o,()=>o.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,o){this.throwIfDisposed(),Ne(this.gl,()=>this.gl.scissor(e,t,n,o))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function p8(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:L2}=C;function z2(r,e,t,n){let o=[];r.forEach(d=>{let h=x.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?o.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(o.push(`uniform sampler2D ${d.name};`),o.push(`uniform int offset${d.name};`))});let s=o.join(`
+`),a=r.map(d=>m8(d,e,n)).join(`
+`),i=e.texShape,l=Ot(),u=h8(l),c,p,m=y8(l);return e.isPacked?(c=f8(e.logicalShape,i),p=x8(l)):(c=d8(e.logicalShape,i),p=g8(l)),n&&(m+=b8),[m,u,p,s,c,a,t].join(`
+`)}function Ip(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return w8(r);case 1:return _8(r);case 2:return k8(r);case 3:return v8(r);case 4:return C8(r);case 5:return I8(r);case 6:return N8(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function B2(r){switch(r.shapeInfo.logicalShape.length){case 0:return S8(r);case 1:return T8(r);case 2:return A8(r);case 3:return E8(r);default:return D8(r)}}function m8(r,e,t=!1){let n="";t?n+=B2(r):n+=Ip(r);let o=r.shapeInfo.logicalShape,s=e.logicalShape;return o.length<=s.length&&(t?n+=$8(r,e):n+=R8(r,e)),n}function f8(r,e){switch(r.length){case 0:return V2();case 1:return F8(r,e);case 2:return M8(r,e);case 3:return O8(r,e);default:return P8(r,e)}}function d8(r,e){switch(r.length){case 0:return V2();case 1:return L8(r,e);case 2:return W8(r,e);case 3:return z8(r,e);case 4:return B8(r,e);case 5:return V8(r,e);case 6:return G8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function h8(r){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${r.texture2D}(textureSampler, uv).r;
     }
-  `}function G5(r){return`
+  `}function g8(r){return`
     void setOutput(float val) {
       ${r.output} = vec4(val, 0, 0, 0);
     }
-  `}function W5(r){return`
+  `}function x8(r){return`
     void setOutput(vec4 val) {
       ${r.output} = val;
     }
-  `}function j5(r){return`${r.version}
+  `}function y8(r){return`${r.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -327,10 +327,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${gX}
-    ${xX}
-    ${yX}
-  `}var gX=`
+    ${j8}
+    ${U8}
+    ${H8}
+  `}var j8=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -342,7 +342,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,xX=`
+`,U8=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -350,7 +350,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,yX=`
+`,H8=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,U5=`
+`,b8=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -370,11 +370,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function C2(){return`
+`;function V2(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function iX(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
+  `}function F8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ${t[1]}.0);
       }
@@ -388,7 +388,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return 2 * (resTexRC.x * ${t[1]} + resTexRC.y);
     }
-  `}function cX(r,e){return e[0]===1?`
+  `}function L8(r,e){return e[0]===1?`
       int getOutputCoords() {
         return int(resultUV.x * ${e[1]}.0);
       }
@@ -402,21 +402,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${e[0]}, ${e[1]}));
       return resTexRC.x * ${e[1]} + resTexRC.y;
     }
-  `}function aX(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),n=o*Math.ceil(r[1]/2);return`
+  `}function O8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[2]/2),o=n*Math.ceil(r[1]/2);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
       int index = resTexRC.x * ${t[1]} + resTexRC.y;
 
-      int b = index / ${n};
-      index -= b * ${n};
+      int b = index / ${o};
+      index -= b * ${o};
 
-      int r = 2 * (index / ${o});
-      int c = imod(index, ${o}) * 2;
+      int r = 2 * (index / ${n});
+      int c = imod(index, ${n}) * 2;
 
       return ivec3(b, r, c);
     }
-  `}function pX(r,e){let t=ks(["r","c","d"],r);return`
+  `}function z8(r,e){let t=vs(["r","c","d"],r);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${e[0]}, ${e[1]}));
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${t}
       return ivec3(r, c, d);
     }
-  `}function lX(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),n=o*Math.ceil(r[r.length-2]/2),s=n,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
+  `}function P8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],n=Math.ceil(r[r.length-1]/2),o=n*Math.ceil(r[r.length-2]/2),s=o,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
       int b${l} = index / ${s};
       index -= b${l} * ${s};
     `+a,i=`b${l}, `+i;return`
@@ -435,15 +435,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       ${a}
 
-      int b = index / ${n};
-      index -= b * ${n};
+      int b = index / ${o};
+      index -= b * ${o};
 
-      int r = 2 * (index / ${o});
-      int c = imod(index, ${o}) * 2;
+      int r = 2 * (index / ${n});
+      int c = imod(index, ${n}) * 2;
 
       return ivec${r.length}(${i});
     }
-  `}function mX(r,e){let t=ks(["r","c","d","d2"],r);return`
+  `}function B8(r,e){let t=vs(["r","c","d","d2"],r);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${e[0]}, ${e[1]}));
@@ -451,7 +451,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${t}
       return ivec4(r, c, d, d2);
     }
-  `}function fX(r,e){let t=ks(["r","c","d","d2","d3"],r);return`
+  `}function V8(r,e){let t=vs(["r","c","d","d2","d3"],r);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
                              ${e[1]}));
@@ -463,7 +463,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function dX(r,e){let t=ks(["r","c","d","d2","d3","d4"],r);return`
+  `}function G8(r,e){let t=vs(["r","c","d","d2","d3","d4"],r);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${e[0]}, ${e[1]}));
@@ -474,22 +474,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function uX(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y.arraysEqual(r,e))return`
+  `}function M8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(x.arraysEqual(r,e))return`
       ivec2 getOutputCoords() {
         return 2 * ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
       }
-    `;let o=Math.ceil(r[1]/2);return`
+    `;let n=Math.ceil(r[1]/2);return`
     ivec2 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
 
       int index = resTexRC.x * ${t[1]} + resTexRC.y;
-      int r = 2 * (index / ${o});
-      int c = imod(index, ${o}) * 2;
+      int r = 2 * (index / ${n});
+      int c = imod(index, ${n}) * 2;
 
       return ivec2(r, c);
     }
-  `}function hX(r,e){return y.arraysEqual(r,e)?`
+  `}function W8(r,e){return x.arraysEqual(r,e)?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
       }
@@ -516,117 +516,117 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${r[1]};
       return ivec2(r, c);
     }
-  `}function Su(r){return`offset${r}`}function Q5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Rt();return`
+  `}function Ru(r){return`offset${r}`}function S8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=Ot();return`
     vec4 ${t}() {
-      return ${o.texture2D}(${e}, halfCR);
+      return ${n.texture2D}(${e}, halfCR);
     }
-  `}function H5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[o,n]=r.shapeInfo.texShape;if(o===1&&n===1)return`
+  `}function w8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[n,o]=r.shapeInfo.texShape;if(n===1&&o===1)return`
       float ${t}() {
         return sampleTexture(${e}, halfCR);
       }
-    `;let[s,a]=r.shapeInfo.texShape,i=Su(e);return`
+    `;let[s,a]=r.shapeInfo.texShape,i=Ru(e);return`
     float ${t}() {
       vec2 uv = uvFromFlat(${s}, ${a}, ${i});
       return sampleTexture(${e}, uv);
     }
-  `}function eX(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,n=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],s=Rt();return`
+  `}function T8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),n=r.shapeInfo.texShape,o=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],s=Ot();return`
     vec4 ${t}(int index) {
       vec2 uv = packedUVfrom1D(
-        ${n[0]}, ${n[1]}, index);
+        ${o[0]}, ${o[1]}, index);
       return ${s.texture2D}(${e}, uv);
     }
-  `}function q5(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
+  `}function _8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
       float ${t}(int index) {
-        ${vp(r)}
+        ${Np(r)}
       }
-    `;let o=r.shapeInfo.texShape,n=o[0],s=o[1];if(s===1&&n===1)return`
+    `;let n=r.shapeInfo.texShape,o=n[0],s=n[1];if(s===1&&o===1)return`
       float ${t}(int index) {
         return sampleTexture(${e}, halfCR);
       }
-    `;let a=Su(e);return s===1?`
+    `;let a=Ru(e);return s===1?`
       float ${t}(int index) {
-        vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${n}.0);
+        vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${o}.0);
         return sampleTexture(${e}, uv);
       }
-    `:n===1?`
+    `:o===1?`
       float ${t}(int index) {
         vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
         return sampleTexture(${e}, uv);
       }
     `:`
     float ${t}(int index) {
-      vec2 uv = uvFromFlat(${n}, ${s}, index + ${a});
+      vec2 uv = uvFromFlat(${o}, ${s}, index + ${a});
       return sampleTexture(${e}, uv);
     }
-  `}function tX(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=n[0],a=n[1],i=Rt();if(n!=null&&y.arraysEqual(e,n))return`
-      vec4 ${o}(int row, int col) {
+  `}function A8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=o[0],a=o[1],i=Ot();if(o!=null&&x.arraysEqual(e,o))return`
+      vec4 ${n}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);
 
         return ${i.texture2D}(${t}, uv);
       }
-    `;let l=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],u=Math.ceil(e[1]/2);return`
-    vec4 ${o}(int row, int col) {
+    `;let l=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=Math.ceil(e[1]/2);return`
+    vec4 ${n}(int row, int col) {
       vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
       return ${i.texture2D}(${t}, uv);
     }
-  `}function K5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape;if(n!=null&&y.arraysEqual(e,n)){let p=n[0],m=n[1];return`
-    float ${o}(int row, int col) {
+  `}function k8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape;if(o!=null&&x.arraysEqual(e,o)){let p=o[0],m=o[1];return`
+    float ${n}(int row, int col) {
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${p}.0);
       return sampleTexture(${t}, uv);
     }
-  `}let{newShape:s,keptDims:a}=y.squeezeShape(e),i=s;if(i.length<e.length){let p=Cp(r,i),m=["row","col"];return`
-      ${kp(p)}
-      float ${o}(int row, int col) {
-        return ${o}(${Ip(m,a)});
+  `}let{newShape:s,keptDims:a}=x.squeezeShape(e),i=s;if(i.length<e.length){let p=Sp(r,i),m=["row","col"];return`
+      ${Ip(p)}
+      float ${n}(int row, int col) {
+        return ${n}(${Tp(m,a)});
       }
     `}if(r.shapeInfo.isUniform)return`
-      float ${o}(int row, int col) {
+      float ${n}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
-        ${vp(r)}
+        ${Np(r)}
       }
-    `;let l=n[0],u=n[1],c=Su(t);return u===1?`
-    float ${o}(int row, int col) {
+    `;let l=o[0],u=o[1],c=Ru(t);return u===1?`
+    float ${n}(int row, int col) {
       float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
       vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
       return sampleTexture(${t}, uv);
     }
   `:l===1?`
-    float ${o}(int row, int col) {
+    float ${n}(int row, int col) {
       float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
       vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
       return sampleTexture(${t}, uv);
     }
   `:`
-  float ${o}(int row, int col) {
+  float ${n}(int row, int col) {
     // Explicitly use integer operations as dot() only works on floats.
     int index = row * ${e[1]} + col + ${c};
     vec2 uv = uvFromFlat(${l}, ${u}, index);
     return sampleTexture(${t}, uv);
   }
-`}function rX(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=Cp(r,p),d=["b","row","col"];return`
-        ${v2(f)}
-        vec4 ${o}(int b, int row, int col) {
-          return ${o}(${Ip(d,m)});
+`}function E8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=r.shapeInfo.texShape,s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=Sp(r,p),d=["b","row","col"];return`
+        ${B2(f)}
+        vec4 ${n}(int b, int row, int col) {
+          return ${n}(${Tp(d,m)});
         }
-      `}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Rt();return`
-    vec4 ${o}(int b, int row, int col) {
+      `}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Ot();return`
+    vec4 ${n}(int b, int row, int col) {
       vec2 uv = packedUVfrom3D(
         ${a}, ${i}, ${u}, ${l}, b, row, col);
       return ${c.texture2D}(${t}, uv);
     }
-  `}function X5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=y.squeezeShape(e),l=a;if(l.length<e.length){let d=Cp(r,l),h=["row","col","depth"];return`
-        ${kp(d)}
-        float ${o}(int row, int col, int depth) {
-          return ${o}(${Ip(h,i)});
+  `}function v8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=x.squeezeShape(e),l=a;if(l.length<e.length){let d=Sp(r,l),h=["row","col","depth"];return`
+        ${Ip(d)}
+        float ${n}(int row, int col, int depth) {
+          return ${n}(${Tp(h,i)});
         }
       `}if(r.shapeInfo.isUniform)return`
-      float ${o}(int row, int col, int depth) {
+      float ${n}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
-                          vec3(${n}, ${s}, 1)));
-        ${vp(r)}
+                          vec3(${o}, ${s}, 1)));
+        ${Np(r)}
       }
-    `;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===n&&m==null)return`
-        float ${o}(int row, int col, int depth) {
+    `;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===o&&m==null)return`
+        float ${n}(int row, int col, int depth) {
           float texR = float(row);
           float texC = dot(vec2(col, depth), vec2(${s}, 1));
           vec2 uv = (vec2(texC, texR) + halfCR) /
@@ -634,50 +634,50 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           return sampleTexture(${t}, uv);
         }
       `;if(p===s&&m==null)return`
-    float ${o}(int row, int col, int depth) {
+    float ${n}(int row, int col, int depth) {
       float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
       float texC = float(depth);
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
       return sampleTexture(${t}, uv);
     }
-  `;let f=Su(t);return`
-      float ${o}(int row, int col, int depth) {
+  `;let f=Ru(t);return`
+      float ${n}(int row, int col, int depth) {
         // Explicitly use integer operations as dot() only works on floats.
-        int index = row * ${n} + col * ${s} + depth + ${f};
+        int index = row * ${o} + col * ${s} + depth + ${f};
         vec2 uv = uvFromFlat(${c}, ${p}, index);
         return sampleTexture(${t}, uv);
       }
-  `}function oX(r){let e=r.shapeInfo.logicalShape,t=e.length,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Rt();return`
-    vec4 ${n}(${p}) {
+  `}function D8(r){let e=r.shapeInfo.logicalShape,t=e.length,n=r.name,o="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Ot();return`
+    vec4 ${o}(${p}) {
       int index = ${m};
       int texR = index / ${l};
       int texC = index - texR * ${l};
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
-      return ${f.texture2D}(${o}, uv);
+      return ${f.texture2D}(${n}, uv);
     }
-  `}function Y5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[3],s=e[2]*n,a=e[1]*s,{newShape:i,keptDims:l}=y.squeezeShape(e);if(i.length<e.length){let d=Cp(r,i),h=["row","col","depth","depth2"];return`
-      ${kp(d)}
-      float ${o}(int row, int col, int depth, int depth2) {
-        return ${o}(${Ip(h,l)});
+  `}function C8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[3],s=e[2]*o,a=e[1]*s,{newShape:i,keptDims:l}=x.squeezeShape(e);if(i.length<e.length){let d=Sp(r,i),h=["row","col","depth","depth2"];return`
+      ${Ip(d)}
+      float ${n}(int row, int col, int depth, int depth2) {
+        return ${n}(${Tp(h,l)});
       }
     `}if(r.shapeInfo.isUniform)return`
-      float ${o}(int row, int col, int depth, int depth2) {
+      float ${n}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
-                          vec4(${a}, ${s}, ${n}, 1)));
-        ${vp(r)}
+                          vec4(${a}, ${s}, ${o}, 1)));
+        ${Np(r)}
       }
     `;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
-      float ${o}(int row, int col, int depth, int depth2) {
+      float ${n}(int row, int col, int depth, int depth2) {
         float texR = float(row);
         float texC =
             dot(vec3(col, depth, depth2),
-                vec3(${s}, ${n}, 1));
+                vec3(${s}, ${o}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${m}.0, ${p}.0);
         return sampleTexture(${t}, uv);
       }
-    `;if(m===n&&u==null)return`
-      float ${o}(int row, int col, int depth, int depth2) {
+    `;if(m===o&&u==null)return`
+      float ${n}(int row, int col, int depth, int depth2) {
         float texR = dot(vec3(row, col, depth),
                          vec3(${e[1]*e[2]}, ${e[2]}, 1));
         float texC = float(depth2);
@@ -685,38 +685,38 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${m}.0, ${p}.0);
         return sampleTexture(${t}, uv);
       }
-    `;let f=Su(t);return`
-    float ${o}(int row, int col, int depth, int depth2) {
+    `;let f=Ru(t);return`
+    float ${n}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${a} + col * ${s} +
-          depth * ${n} + depth2;
+          depth * ${o} + depth2;
       vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
       return sampleTexture(${t}, uv);
     }
-  `}function Z5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=y.squeezeShape(e);if(l.length<e.length){let h=Cp(r,l),g=["row","col","depth","depth2","depth3"];return`
-      ${kp(h)}
-      float ${o}(int row, int col, int depth, int depth2, int depth3) {
-        return ${o}(${Ip(g,u)});
+  `}function I8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),o=e[4],s=e[3]*o,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=x.squeezeShape(e);if(l.length<e.length){let h=Sp(r,l),g=["row","col","depth","depth2","depth3"];return`
+      ${Ip(h)}
+      float ${n}(int row, int col, int depth, int depth2, int depth3) {
+        return ${n}(${Tp(g,u)});
       }
     `}if(r.shapeInfo.isUniform)return`
-      float ${o}(int row, int col, int depth, int depth2, int depth3) {
+      float ${n}(int row, int col, int depth, int depth2, int depth3) {
         float index = dot(
           vec4(row, col, depth, depth2),
-          vec4(${i}, ${a}, ${s}, ${n})) +
+          vec4(${i}, ${a}, ${s}, ${o})) +
           depth3;
-        ${vp(r)}
+        ${Np(r)}
       }
     `;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
-      float ${o}(int row, int col, int depth, int depth2, int depth3) {
+      float ${n}(int row, int col, int depth, int depth2, int depth3) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
-                         vec4(${a}, ${s}, ${n}, 1));
+                         vec4(${a}, ${s}, ${o}, 1));
         vec2 uv = (vec2(texC, texR) + halfCR) /
                    vec2(${f}.0, ${m}.0);
         return sampleTexture(${t}, uv);
       }
-    `;if(f===n&&c==null)return`
-      float ${o}(int row, int col, int depth, int depth2, int depth3) {
+    `;if(f===o&&c==null)return`
+      float ${n}(int row, int col, int depth, int depth2, int depth3) {
         float texR = dot(
           vec4(row, col, depth, depth2),
           vec4(${e[1]*e[2]*e[3]},
@@ -726,22 +726,22 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${m}.0);
         return sampleTexture(${t}, uv);
       }
-    `;let d=Su(t);return`
-    float ${o}(int row, int col, int depth, int depth2, int depth3) {
+    `;let d=Ru(t);return`
+    float ${n}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${i} + col * ${a} + depth * ${s} +
-          depth2 * ${n} + depth3 + ${d};
+          depth2 * ${o} + depth3 + ${d};
       vec2 uv = uvFromFlat(${m}, ${f}, index);
       return sampleTexture(${t}, uv);
     }
-  `}function J5(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=y.squeezeShape(e);if(n.length<e.length){let g=Cp(r,n),x=["row","col","depth","depth2","depth3","depth4"];return`
-      ${kp(g)}
-      float ${o}(int row, int col, int depth,
+  `}function N8(r){let e=r.shapeInfo.logicalShape,t=r.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:o,keptDims:s}=x.squeezeShape(e);if(o.length<e.length){let g=Sp(r,o),y=["row","col","depth","depth2","depth3","depth4"];return`
+      ${Ip(g)}
+      float ${n}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${o}(${Ip(x,s)});
+        return ${n}(${Tp(y,s)});
       }
     `}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;if(r.shapeInfo.isUniform)return`
-      float ${o}(int row, int col, int depth,
+      float ${n}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
         int index = round(dot(
           vec4(row, col, depth, depth2),
@@ -749,10 +749,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${a}, 1)));
-        ${vp(r)}
+        ${Np(r)}
       }
     `;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
-      float ${o}(int row, int col, int depth,
+      float ${n}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         int texR = row;
         float texC = dot(vec4(col, depth, depth2, depth3),
@@ -763,7 +763,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         return sampleTexture(${t}, uv);
       }
     `;if(d===a&&p==null)return`
-      float ${o}(int row, int col, int depth,
+      float ${n}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
         float texR = dot(vec4(row, col, depth, depth2),
           vec4(${e[1]*e[2]*e[3]*e[4]},
@@ -775,8 +775,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${d}.0, ${f}.0);
         return sampleTexture(${t}, uv);
       }
-    `;let h=Su(t);return`
-    float ${o}(int row, int col, int depth,
+    `;let h=Ru(t);return`
+    float ${n}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${c} + col * ${u} + depth * ${l} +
@@ -784,44 +784,44 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${f}, ${d}, index);
       return sampleTexture(${t}, uv);
     }
-  `}function vp(r){let e=r.name,t=y.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
+  `}function Np(r){let e=r.name,t=x.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
     for (int i = 0; i < ${t}; i++) {
       if (i == index) {
         return ${e}[i];
       }
     }
-  `}function nX(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=_2(r.shapeInfo.logicalShape,e.logicalShape),l=Le(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
-`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+u]}`).join(", ");let f="return outputValue;",h=y.sizeFromShape(r.shapeInfo.logicalShape)===1,x=y.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!x)f=`
+  `}function $8(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=L2(r.shapeInfo.logicalShape,e.logicalShape),l=Ve(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
+`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+u]}`).join(", ");let f="return outputValue;",h=x.sizeFromShape(r.shapeInfo.logicalShape)===1,y=x.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!y)f=`
       return vec4(outputValue.xy, outputValue.xy);
-    `;else if(h&&!x)a===1?f=`
+    `;else if(h&&!y)a===1?f=`
         return vec4(outputValue.x, outputValue.x, 0., 0.);
       `:f=`
         return vec4(outputValue.x);
       `;else if(i.length){let b=s-2,w=s-1;i.indexOf(b)>-1&&i.indexOf(w)>-1?f="return vec4(outputValue.x);":i.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
-    vec4 ${n}() {
+    vec4 ${o}() {
       ${l} coords = getOutputCoords();
       ${c}
-      vec4 outputValue = get${o}(${m});
+      vec4 outputValue = get${n}(${m});
       ${f}
     }
-  `}function sX(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&y.arraysEqual(a,s))return`
-      float ${n}() {
+  `}function R8(r,e){let t=r.name,n=t.charAt(0).toUpperCase()+t.slice(1),o="get"+n+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&x.arraysEqual(a,s))return`
+      float ${o}() {
         return sampleTexture(${t}, resultUV);
       }
-    `;let u=Le(l),c=_2(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
+    `;let u=Ve(l),c=L2(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
 `);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
-    float ${n}() {
+    float ${o}() {
       ${u} coords = getOutputCoords();
       ${m}
-      return get${o}(${d});
+      return get${n}(${d});
     }
-  `}function Le(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Cp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function Ip(r,e){return e.map(t=>r[t]).join(", ")}function I2(r,e,t,o){let n=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},l=k2(s,i,n,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);W().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f<e.variableNames.length;f++){let d=e.variableNames[f],h=!1;m[d]=r.getUniformLocation(u,d,h),m[`offset${d}`]=r.getUniformLocation(u,`offset${d}`,h)}return{program:e,source:l,webGLProgram:u,uniformLocations:m,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p}}function N2(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,o)=>{let n=t.logicalShape,s=e[o],a=s.shape;if(!y.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!y.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function S2(r,e,t,o,n){N2(e.inShapeInfos,t),N2([e.outShapeInfo],[o]);let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),W().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(y.sizeFromShape(i.shape)<2)r.gl.uniform1f(c,i.uniformValues[0]);else{let m=i.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),r.gl.uniform1fv(c,m)}return}i.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,i.texData.slice.flatOffset),r.setInputMatrixTexture(i.texData.texture,c,l)}}),n!=null&&n(r,e.webGLProgram),r.executeProgram()}function T2(r,e,t){let o="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,l=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${l}_${i}`});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n,s}var{addImpl:A2,bincountImpl:rx,bincountReduceImpl:E2,ceilImpl:D2,concatImpl:$2,expImpl:R2,expm1Impl:F2,floorImpl:O2,gatherV2Impl:P2,greaterImpl:M2,lessImpl:L2,linSpaceImpl:z2,logImpl:B2,maxImpl:V2,maximumImpl:G2,minimumImpl:W2,multiplyImpl:j2,negImpl:U2,prodImpl:H2,rangeImpl:q2,rsqrtImpl:K2,simpleAbsImpl:ox,sliceImpl:X2,stridedSliceImpl:Y2,subImpl:Z2,tileImpl:J2,topKImpl:Q2,transposeImpl:Tu,uniqueImpl:eD}=Mg;function Fk(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function Wt(r,e){return e===1?[r]:Fk(r,e)}function tD(r,e){if(r===1)return"rc";let t="";for(let o=0;o<r;o++)t+=e[o],o<r-1&&(t+=",");return t}var Ok=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
+  `}function Ve(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Sp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function Tp(r,e){return e.map(t=>r[t]).join(", ")}function G2(r,e,t,n){let o=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},l=z2(s,i,o,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);W().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f<e.variableNames.length;f++){let d=e.variableNames[f],h=!1;m[d]=r.getUniformLocation(u,d,h),m[`offset${d}`]=r.getUniformLocation(u,`offset${d}`,h)}return{program:e,source:l,webGLProgram:u,uniformLocations:m,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p}}function W2(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,n)=>{let o=t.logicalShape,s=e[n],a=s.shape;if(!x.arraysEqual(o,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${o} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!x.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function j2(r,e,t,n,o){W2(e.inShapeInfos,t),W2([e.outShapeInfo],[n]);let s=n.texData.texture,a=n.texData.texShape;n.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),W().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(x.sizeFromShape(i.shape)<2)r.gl.uniform1f(c,i.uniformValues[0]);else{let m=i.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),r.gl.uniform1fv(c,m)}return}i.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,i.texData.slice.flatOffset),r.setInputMatrixTexture(i.texData.texture,c,l)}}),o!=null&&o(r,e.webGLProgram),r.executeProgram()}function U2(r,e,t){let n="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,l=a.isUniform?"uniform":a.texData.texShape;n+=`${a.shape}_${l}_${i}`});let o=r.userCode,s=r.constructor.name;return s+="_"+n+"_"+o,s}var{addImpl:H2,bincountImpl:ix,bincountReduceImpl:q2,ceilImpl:K2,concatImpl:X2,expImpl:Y2,expm1Impl:Z2,floorImpl:J2,gatherV2Impl:Q2,greaterImpl:eD,lessImpl:tD,linSpaceImpl:rD,logImpl:nD,maxImpl:oD,maximumImpl:sD,minimumImpl:iD,multiplyImpl:aD,negImpl:lD,prodImpl:uD,rangeImpl:cD,rsqrtImpl:pD,simpleAbsImpl:ax,sliceImpl:mD,stridedSliceImpl:fD,subImpl:dD,tileImpl:hD,topKImpl:gD,transposeImpl:Fu,uniqueImpl:xD}=Vg;function Kk(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function Wt(r,e){return e===1?[r]:Kk(r,e)}function yD(r,e){if(r===1)return"rc";let t="";for(let n=0;n<r;n++)t+=e[n],n<r-1&&(t+=",");return t}var Xk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let o=Wt("rc",t),n=Le(t),s=bX(t,e,o),a=wX(t,e[e.length-1],e[e.length-2],o),i=_X(e,o);this.userCode=`
+      `;else{let n=Wt("rc",t),o=Ve(t),s=q8(t,e,n),a=K8(t,e[e.length-1],e[e.length-2],n),i=X8(e,n);this.userCode=`
         void main() {
-          ${n} rc = getOutputCoords();
+          ${o} rc = getOutputCoords();
 
           if(${s}) {
             setOutput(vec4(0));
@@ -831,33 +831,33 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             setOutput(vec4(${i}));
           }
         }
-      `}}};function kX(r,e){let t=[];for(let o=0;o<=1;o++)for(let n=0;n<=1;n++){let s=`${o===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function bX(r,e,t){if(r===1)return`rc > ${e[0]}`;let o="";for(let n=r-2;n<r;n++)o+=`${t[n]} >= ${e[n]}`,n<r-1&&(o+="||");return o}function wX(r,e,t,o){if(r===1)return"";let n=o.slice(-2);return`
-    int r = ${n[0]};
-    int c = ${n[1]};
+      `}}};function Y8(r,e){let t=[];for(let n=0;n<=1;n++)for(let o=0;o<=1;o++){let s=`${n===0?"r":"rp1"}, ${o===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function q8(r,e,t){if(r===1)return`rc > ${e[0]}`;let n="";for(let o=r-2;o<r;o++)n+=`${t[o]} >= ${e[o]}`,o<r-1&&(n+="||");return n}function K8(r,e,t,n){if(r===1)return"";let o=n.slice(-2);return`
+    int r = ${o[0]};
+    int c = ${o[1]};
     int rp1 = r + 1;
     int cp1 = c + 1;
 
     bool cEdge = cp1 >= ${e};
     bool rEdge = rp1 >= ${t};
-  `}function _X(r,e){let t=r.length,o=kX(t,e);return t===1?`getA(rc),
+  `}function X8(r,e){let t=r.length,n=Y8(t,e);return t===1?`getA(rc),
             rc + 1 >= ${r[0]} ? 0. : getA(rc + 1),
-            0, 0`:`getA(${o[0]}),
-          cEdge ? 0. : getA(${o[1]}),
-          rEdge ? 0. : getA(${o[2]}),
-          rEdge || cEdge ? 0. : getA(${o[3]})`}var Tf=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2==1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=`
+            0, 0`:`getA(${n[0]}),
+          cEdge ? 0. : getA(${n[1]}),
+          rEdge ? 0. : getA(${n[2]}),
+          rEdge || cEdge ? 0. : getA(${n[3]})`}var $f=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let o=0;o<4;o++){let s="thisRC = rc;";o%2==1&&(s+="thisRC.z += 1;"),o>1&&(s+="thisRC.y += 1;"),n+=`
         ${s}
-        ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
+        ${o>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
 
           ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
           vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
 
-          result[${n}] =
+          result[${o}] =
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
-        ${n>0?"}":""}
+        ${o>0?"}":""}
       `}this.userCode=`
-      ${vX(t)}
-      ${_p(e)}
+      ${Z8(t)}
+      ${Cp(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -868,16 +868,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         int rows = ${e[1]};
         int cols = ${e[2]};
 
-        ${o}
+        ${n}
 
         setOutput(result);
       }
-    `}};function vX(r){return`
+    `}};function Z8(r){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${ks(["r","c","d"],r)}
+      ${vs(["r","c","d"],r)}
       return ivec3(r, c, d);
     }
-  `}var Pk=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=oD(t,o),s=nD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=rD(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return n===vr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===vr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===vr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===vr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===vr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=oD(o,n),a=nD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=rD(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),l=W().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function CX(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function rD(r,e,t,o,n){let s=IX(e,o),a;if(n){let[l,u]=si(r[0],r[1]);a=l*u}else{let[l,u]=Nu(r[0],r[1]);a=l*u}let i=CX(t,s);return a*i}function IX(r,e){switch(r){case vr.PACKED_2X2_FLOAT32:return Qg(e);case vr.PACKED_2X2_FLOAT16:return ex(e);case vr.UNPACKED_FLOAT32:return Yg(e);case vr.UNPACKED_FLOAT16:return Zg(e);case vr.PACKED_4X1_UNSIGNED_BYTE:return Jg(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function NX(r){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?vr.PACKED_2X2_FLOAT32:vr.UNPACKED_FLOAT32:r?vr.PACKED_2X2_FLOAT16:vr.UNPACKED_FLOAT16}function oD(r,e){if(r===Fr.UPLOAD)return vr.PACKED_2X2_FLOAT32;if(r===Fr.RENDER||r==null)return NX(e);if(r===Fr.DOWNLOAD||r===Fr.PIXELS)return vr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function nD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var mo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+  `}var Yk=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let o=wD(t,n),s=_D(e,o,n);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=bD(e,o,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return o===kr.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===kr.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===kr.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===kr.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===kr.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,o){if(this.freeTextures==null)return;let s=wD(n,o),a=_D(t,s,o);a in this.freeTextures||(this.freeTextures[a]=[]);let i=bD(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,o),l=W().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function J8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function bD(r,e,t,n,o){let s=Q8(e,n),a;if(o){let[l,u]=li(r[0],r[1]);a=l*u}else{let[l,u]=$u(r[0],r[1]);a=l*u}let i=J8(t,s);return a*i}function Q8(r,e){switch(r){case kr.PACKED_2X2_FLOAT32:return nx(e);case kr.PACKED_2X2_FLOAT16:return ox(e);case kr.UNPACKED_FLOAT32:return ex(e);case kr.UNPACKED_FLOAT16:return tx(e);case kr.PACKED_4X1_UNSIGNED_BYTE:return rx(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function eX(r){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?kr.PACKED_2X2_FLOAT32:kr.UNPACKED_FLOAT32:r?kr.PACKED_2X2_FLOAT16:kr.UNPACKED_FLOAT16}function wD(r,e){if(r===Rr.UPLOAD)return kr.PACKED_2X2_FLOAT32;if(r===Rr.RENDER||r==null)return eX(e);if(r===Rr.DOWNLOAD||r===Rr.PIXELS)return kr.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function _D(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var hn=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -888,11 +888,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},xr="if (isnan(x)) return x;",sD="return x;",Mk="return abs(x);";var iD="return (x >= 0.0) ? x : (exp(x) - 1.0);",aD=xr+`
+    `}},yr="if (isnan(x)) return x;",kD="return x;",Zk="return abs(x);";var vD="return (x >= 0.0) ? x : (exp(x) - 1.0);",CD=yr+`
   return (x < 0.0) ? 0.0 : x;
-`,lD=xr+`
+`,ID=yr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,Af="return x;";var uD="return x;",cD=`
+`,Rf="return x;";var ND="return x;",SD=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,pD=`
+`,TD=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -911,7 +911,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,mD=`
+`,AD=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -921,7 +921,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,vs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+`,Cs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -932,17 +932,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}};var Lk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,o=Wt("rc",t),n=Le(t),s=tD(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
+    `}};var Jk=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Wt("rc",t),o=Ve(t),s=yD(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
       void main() {
-        ${n} rc = getOutputCoords();
+        ${o} rc = getOutputCoords();
         vec4 packedInput = getA(${s});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}};var SX=$r.whereImpl,TX=1e-7,AX=1e-4,nx={};function EX(r){return r in nx||(nx[r]={}),nx[r]}var DX=128,$X=600;function RX(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*$X/1024/1024}var Au=class extends $s{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Bo(W().getNumber("WEBGL_VERSION"));this.binaryCache=EX(W().getNumber("WEBGL_VERSION")),this.gpgpu=new tx(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Pk(this.gpgpu),this.numMBBeforeWarning=RX(),this.texData=new Va(this,Zo())}nextDataId(){return Au.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,o){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:Fr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:Fr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new vs(i,Af):m=new mo(i,Af);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let p;if(n==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=N.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new vs(n,Af):d=new mo(n,Af);let h=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...dl(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=N.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=y.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Zo().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>y.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,o)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let o=e[t];if(!U_(o))throw W().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=y.sizeFromShape(t);if(W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...dl(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=W().getBool("WEBGL_PACK")&&n===!0,i=a?Nf(t):t,l=a?new hk(i):new dk(i),u=this.runWebGLProgram(l,[{shape:i,dtype:o,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}timerAvailable(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=y.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=y.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}getCPUBackend(){return W().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Zo().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=DX){let o=this.getCPUBackend();return!W().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&o==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),o!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&y.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return SX(e.shape,t)}packedUnaryOp(e,t,o){let n=new vs(e.shape,t),s=this.compileAndRun(n,[e],o);return Zo().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=ox(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Mk,e.dtype);let t=new mo(e.shape,Mk),o=this.compileAndRun(t,[e]);return Zo().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){let{dataId:n}=this.makeTensorInfo(e,t,o);return Zo().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Lk(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Ok(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Ca(e.shape),...Ia(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Ca(t),...Ia(t)],a=new Tf(s,o),i=!0,l=this.runWebGLProgram(a,[n],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:o,shape:n,dtype:s}=t,a=Nf(n),i;o?i=new fk(a):i=new mk(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,o,n,s=!1){let a=this.makeTensorInfo(e.outputShape,o),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===fl.DENSE){let g=dl(e.outputShape);i.texShape=g.map(x=>x*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),y.sizeFromShape(a.shape)===0)return i.values=y.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let x=this.texData.get(g.dataId);if(x.texture==null){if(!e.packedInputs&&y.sizeFromShape(g.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:x.values};e.packedInputs&&(x.isPacked=!0,x.shape=g.shape)}else if(!!x.isPacked!=!!e.packedInputs)g=x.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),x=this.texData.get(g.dataId);else if(x.isPacked&&!hl(x.shape,g.shape)){let b=g,w=g.shape;g.shape=x.shape,g=this.packedReshape(g,w),l.push(g),x=this.texData.get(g.dataId),b.shape=w}return this.uploadToGPU(g.dataId),{shape:g.shape,texData:x,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=T2(e,u,c),m=this.getAndSaveBinary(p,()=>I2(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;f&&(d=this.startTimer()),S2(this.gpgpu,m,u,c,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));let h=W().get("WEBGL_FLUSH_THRESHOLD");if(h>0){let g=y.now();g-this.lastGlFlushTime>h&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),g}return a}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(W().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=V(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=W().getBool("DEBUG");W().set("DEBUG",!1);let t=this.abs(le(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?TX:AX}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let p=t.texShape;if(p==null&&(p=nk(o,l),t.texShape=p),s!=null){let m=Nf(o),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=si(p[0],p[1]),f=new xk(m,[h,d],g)):f=new gk(m,[h,d],g);let x=this.makeTensorInfo([h,d],n);g?this.texData.get(x.dataId).usage=Fr.PIXELS:this.texData.get(x.dataId).usage=Fr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(x.dataId),d,h,s);let b=!0,w=this.runWebGLProgram(f,[x],n,null,b),_=this.texData.get(w.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(x),this.texData.delete(w.dataId),t.values=null,u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(p,i,n,l);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return this.releaseGPUData(e),t!=null&&(o.values=FX(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}};Au.nextDataId=0;function FX(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;o<t.length;++o)t[o]=Math.round(r[o]);return t}else throw new Error(`Unknown dtype ${e}`)}var zk="3.3.0";function fD(){W().set("WEBGL_FORCE_F16_TEXTURES",!0)}jl.isBrowser()&&Xc("webgl",()=>new Au,2);var kXe={forceHalfFloat:fD};var sx=`
+    `}};var tX=Dr.whereImpl,rX=1e-7,nX=1e-4,lx={};function oX(r){return r in lx||(lx[r]={}),lx[r]}var sX=128,iX=600;function aX(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*iX/1024/1024}var Ou=class extends Os{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Vn(W().getNumber("WEBGL_VERSION"));this.binaryCache=oX(W().getNumber("WEBGL_VERSION")),this.gpgpu=new sx(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new Yk(this.gpgpu),this.numMBBeforeWarning=aX(),this.texData=new qa(this,ds())}nextDataId(){return Ou.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,n){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let o={id:this.nextDataId()};return this.texData.set(o,{shape:t,dtype:n,values:e,usage:Rr.UPLOAD,refCount:1}),o}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,o,s){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),o==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:o,values:t,usage:Rr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:o,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Cs(i,Rf):m=new hn(i,Rf);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:o}],o),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(n!=null)return this.convertAndCacheOnCPU(e);if(o==="string")return n;let u=this.activeTimers!=null,c;u&&(c=x.now());let p;if(o==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=C.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=x.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:n,shape:o,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Cs(o,Rf):d=new hn(o,Rf);let h=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(n!=null)return this.convertAndCacheOnCPU(e);if(!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...yl(o))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=C.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=x.sizeFromShape(o);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ds().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(o=>x.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ce(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!sk(n))throw W().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:o}=this.texData.get(e),s=x.sizeFromShape(t);if(W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...yl(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=W().getBool("WEBGL_PACK")&&o===!0,i=a?Ef(t):t,l=a?new Ak(i):new Tk(i),u=this.runWebGLProgram(l,[{shape:i,dtype:n,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}timerAvailable(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],o=!1;this.programTimersStack==null?(this.programTimersStack=n,o=!0):this.activeTimers.push(n),this.activeTimers=n,e();let s=x.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=x.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,o&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=x.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:x.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=x.now(),e)}async getQueryTime(e){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:o,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(o,n),this.textureManager.releaseTexture(t,o,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=sX){return W().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&x.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){C.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return tX(e.shape,t)}packedUnaryOp(e,t,n){let o=new Cs(e.shape,t),s=this.compileAndRun(o,[e],n);return ds().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let o=ax(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,o)}if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Zk,e.dtype);let t=new hn(e.shape,Zk),n=this.compileAndRun(t,[e]);return ds().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let o;if(t==="string"&&n!=null&&n.length>0&&x.isString(n[0])){let s=n.map(a=>x.encodeString(a));o=this.write(s,e,t)}else o=this.write(n,e,t);return this.texData.get(o).usage=null,{dataId:o,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:o}=this.makeTensorInfo(e,t,n);return ds().makeTensorFromDataId(o,e,t,this)}unpackTensor(e){let t=new Jk(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Xk(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Ea(e.shape),...Da(e.shape)],o={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Ea(t),...Da(t)],a=new $f(s,n),i=!0,l=this.runWebGLProgram(a,[o],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:o,dtype:s}=t,a=Ef(o),i;n?i=new Sk(a):i=new Nk(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:o,dataId:u.dataId}}runWebGLProgram(e,t,n,o,s=!1){let a=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===xl.DENSE){let g=yl(e.outputShape);i.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),x.sizeFromShape(a.shape)===0)return i.values=x.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&x.sizeFromShape(g.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}else if(!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!bl(y.shape,g.shape)){let b=g,w=g.shape;g.shape=y.shape,g=this.packedReshape(g,w),l.push(g),y=this.texData.get(g.dataId),b.shape=w}return this.uploadToGPU(g.dataId),{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=U2(e,u,c),m=this.getAndSaveBinary(p,()=>G2(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;f&&(d=this.startTimer()),j2(this.gpgpu,m,u,c,o),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));let h=W().get("WEBGL_FLUSH_THRESHOLD");if(h>0){let g=x.now();g-this.lastGlFlushTime>h&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),g}return a}compileAndRun(e,t,n,o,s=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,o,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(W().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=V(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=W().getBool("DEBUG");W().set("DEBUG",!1);let t=this.abs(le(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?rX:nX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:o,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=x.now());let p=t.texShape;if(p==null&&(p=yk(n,l),t.texShape=p),s!=null){let m=Ef(n),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=li(p[0],p[1]),f=new Dk(m,[h,d],g)):f=new Ek(m,[h,d],g);let y=this.makeTensorInfo([h,d],o);g?this.texData.get(y.dataId).usage=Rr.PIXELS:this.texData.get(y.dataId).usage=Rr.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),d,h,s);let b=!0,w=this.runWebGLProgram(f,[y],o,null,b),_=this.texData.get(w.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(w.dataId),t.values=null,u&&(this.uploadWaitMs+=x.now()-c)}else{let m=this.acquireTexture(p,i,o,l);t.texture=m}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:o}=n;return this.releaseGPUData(e),t!=null&&(n.values=lX(t,o)),n.values}acquireTexture(e,t,n,o){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,o)}computeBytes(e,t){return e[0]*e[1]*x.bytesPerElement(t)}};Ou.nextDataId=0;function lX(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let n=0;n<t.length;++n)t[n]=Math.round(r[n]);return t}else throw new Error(`Unknown dtype ${e}`)}var Qk="3.4.0";function ED(){W().set("WEBGL_FORCE_F16_TEXTURES",!0)}Yl.isBrowser()&&Jc("webgl",()=>new Ou,2);var IXe={forceHalfFloat:ED};var ux=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`;var cn=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
+`;var uo=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -952,17 +952,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}};var gl=`
+    `}};var wl=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;var Cs=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`
+`;var Is=class{constructor(e,t,n,o=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=C.assertAndGetBroadcastShape(t,n);let s=this.outputShape.length,a="";if(o)if(s===0||x.sizeFromShape(this.outputShape)===1)a=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
         `;else if(a=`
-          ${Le(s)} coords = getOutputCoords();
+          ${Ve(s)} coords = getOutputCoords();
         `,s===1)a+=`
             result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
             result.z = 0.;
@@ -989,21 +989,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function jt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var dD={kernelName:Ko,backendName:"webgl",kernelFunc:jt};function fo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=jt({inputs:{x:o},backend:t}),l=jt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var hD={kernelName:xc,backendName:"webgl",kernelFunc:fo};var Bk="return (a < 0.) ? b * a : a;",Vk=`
+    `}};function jt(r){let{inputs:e,backend:t}=r,{x:n}=e;return t.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var DD={kernelName:Xn,backendName:"webgl",kernelFunc:jt};function gn(r){let{inputs:e,backend:t}=r,{real:n,imag:o}=e,s=t.makeTensorInfo(n.shape,"complex64"),a=t.texData.get(s.dataId),i=jt({inputs:{x:n},backend:t}),l=jt({inputs:{x:o},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var $D={kernelName:bc,backendName:"webgl",kernelFunc:gn};var ev="return (a < 0.) ? b * a : a;",tv=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function OX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",y.createScalarValue(s,"float32")),i=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cs(Vk,n.shape,a.shape):new cn(Bk,n.shape,a.shape),l=t.runWebGLProgram(i,[n,a],n.dtype);return t.disposeIntermediateTensorInfo(a),l}var gD={kernelName:Ln,backendName:"webgl",kernelFunc:OX};var Gk="return (a < 0.) ? b * a : a;",Wk=`
+`;function uX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{alpha:s}=n,a=t.makeTensorInfo([],"float32",x.createScalarValue(s,"float32")),i=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Is(tv,o.shape,a.shape):new uo(ev,o.shape,a.shape),l=t.runWebGLProgram(i,[o,a],o.dtype);return t.disposeIntermediateTensorInfo(a),l}var RD={kernelName:Po,backendName:"webgl",kernelFunc:uX};var rv="return (a < 0.) ? b * a : a;",nv=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function PX(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cs(Wk,o.shape,n.shape):new cn(Gk,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)}var xD={kernelName:Yn,backendName:"webgl",kernelFunc:PX};var ix="if (isnan(x)) return x;",yD=`
+`;function cX(r){let{inputs:e,backend:t}=r,{x:n,alpha:o}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Is(nv,n.shape,o.shape):new uo(rv,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)}var FD={kernelName:Xo,backendName:"webgl",kernelFunc:cX};var cx="if (isnan(x)) return x;",OD=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,bD=`
+`,PD=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function _e({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,l=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new vs(a.shape,e):c=new mo(a.shape,r),i.runWebGLProgram(c,[a],l)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(o&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[_,k]=w,D={dataId:_.dataId,dtype:_.dtype,shape:l.shape},T={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new cn(r,l.shape,u.shape);return c.runWebGLProgram(R,[D,T],ir(_.dtype,k.dtype))}),b=fo({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let p=s||ir(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&n!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,x]=n(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(x,p),w=c.texData.get(b.dataId);return w.values=g,b}let m=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Cs(e,l.shape,u.shape,t):f=new cn(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function xl(r,e=!1){if(r==="linear")return e?uD:sD;if(r==="relu")return e?pD:aD;if(r==="elu")return e?cD:iD;if(r==="relu6")return e?mD:lD;if(r==="prelu")return e?Wk:Gk;if(r==="leakyrelu")return e?Vk:Bk;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Ef=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o;let c=n?e[1]:e[2],p=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",x="";i&&(l?g=`vec4 activation(vec4 a) {
+`;function _e({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:n}){return({inputs:o,backend:s})=>{let{x:a}=o,i=s,l=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Cs(a.shape,e):c=new hn(a.shape,r),i.runWebGLProgram(c,[a],l)}}function st({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:n=!1,cpuKernelImpl:o,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(n&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,y]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[_,I]=w,E={dataId:_.dataId,dtype:_.dtype,shape:l.shape},$={dataId:I.dataId,dtype:I.dtype,shape:u.shape},D=new uo(r,l.shape,u.shape);return c.runWebGLProgram(D,[E,$],ar(_.dtype,I.dtype))}),b=gn({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),b}let p=s||ar(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&o!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,y]=o(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(y,p),w=c.texData.get(b.dataId);return w.values=g,b}let m=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Is(e,l.shape,u.shape,t):f=new uo(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function _l(r,e=!1){if(r==="linear")return e?ND:kD;if(r==="relu")return e?TD:CD;if(r==="elu")return e?SD:vD;if(r==="relu6")return e?AD:ID;if(r==="prelu")return e?nv:rv;if(r==="leakyrelu")return e?tv:ev;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var Ff=class{constructor(e,t,n,o=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let c=o?e[1]:e[2],p=Math.ceil(c/2),m=o?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=o?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",y="";i&&(l?g=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:u?g=`vec4 activation(vec4 a) {
@@ -1011,7 +1011,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           ${i}
         }`:g=`vec4 activation(vec4 x) {
           ${i}
-        }`,x="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",_="rc.x";e[0]<t[0]?w=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
+        }`,y="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",_="rc.x";e[0]<t[0]?w=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
       ${g}
 
       const float sharedDimension = ${p}.0;
@@ -1038,11 +1038,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${b}
 
-        ${x}
+        ${y}
 
         setOutput(result);
       }
-    `}};var jk={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},ax=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
+    `}};var ov={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},px=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=C.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1055,7 +1055,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}};var wD="return a * b;";function Uk(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=N.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),u=new ax(jk.REAL,o.shape,n.shape),c=new ax(jk.IMAG,o.shape,n.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=fo({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),[u,c]=j2(o.shape,n.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Cs(wD,o.shape,n.shape):a=new cn(wD,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var _D={kernelName:Hn,backendName:"webgl",kernelFunc:Uk};function kD(r,e,t){let o=[Ca(r.shape),...Ia(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Ca(e),...Ia(e)],a=new Tf(s,o),i=!0,l=t.runWebGLProgram(a,[n],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=y.sizeFromShape(n.shape),l=y.inferFromImplicitShape(s,i),u=y.sizeFromShape(l);y.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!hl(n.shape,l)&&!(c.texture!==null&&hl(c.shape,l))?kD(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var vD={kernelName:Gs,backendName:"webgl",kernelFunc:pe};var lx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${y.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%o>0&&(c=`
+    `}};var MD="return a * b;";function Of(r){let{inputs:e,backend:t}=r,{a:n,b:o}=e,s=C.upcastType(n.dtype,o.dtype);if(n.dtype==="complex64"){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),u=new px(ov.REAL,n.shape,o.shape),c=new px(ov.IMAG,n.shape,o.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:n.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:o.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:o.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=gn({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([n,o])){let i=t.texData.get(n.dataId),l=t.texData.get(o.dataId),[u,c]=aD(n.shape,o.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Is(MD,n.shape,o.shape):a=new uo(MD,n.shape,o.shape),t.runWebGLProgram(a,[n,o],s)}var LD={kernelName:Uo,backendName:"webgl",kernelFunc:Of};function zD(r,e,t){let n=[Ea(r.shape),...Da(r.shape)],o={dtype:r.dtype,shape:n,dataId:r.dataId},s=[Ea(e),...Da(e)],a=new $f(s,n),i=!0,l=t.runWebGLProgram(a,[o],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function ue(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{shape:s}=n,a=t,i=x.sizeFromShape(o.shape),l=x.inferFromImplicitShape(s,i),u=x.sizeFromShape(l);x.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${o.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(o.dataId);return c.isPacked&&!bl(o.shape,l)&&!(c.texture!==null&&bl(c.shape,l))?zD(o,l,a):(a.incRef(o.dataId),{dataId:o.dataId,shape:l,dtype:o.dtype})}var BD={kernelName:Us,backendName:"webgl",kernelFunc:ue};var mx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i=Math.floor(n/4)*4,l=n%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${x.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%n>0&&(c=`
         if (inIdx < 0 || inIdx >= ${s}) {
           return 0.0;
         }
@@ -1071,7 +1071,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int outIdx = coords[1];
-        int inOffset = outIdx * ${o};
+        int inOffset = outIdx * ${n};
 
         float sumValue = 0.0;
 
@@ -1108,7 +1108,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}};var Hk=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,p=o%4,m=`
+    `}};var sv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:o,inSize:s,outSize:a}=e;this.outputShape=[o,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(n/4)*4,p=n%4,m=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1125,7 +1125,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         bool reducedAnyValue = any(values);
         float floatedReducedAnyValue = float(reducedAnyValue);
         anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
-      `,f="bvec4");let d="";s%o>0&&(d=`
+      `,f="bvec4");let d="";s%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${s}) {
           return initializationValue;
         }
@@ -1142,7 +1142,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int outIdx = coords[1];
-        int inOffset = outIdx * ${o};
+        int inOffset = outIdx * ${n};
 
         vec4 minMaxValue = vec4(${i});
         float prodValue = 1.0;
@@ -1193,21 +1193,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${u});
       }
-    `}};function MX(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function To(r,e,t,o){let n=MX(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:l,outSize:u}=n[a],c,p;t==="mean"?c=a===0?new lx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new lx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Hk({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=o.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(p)}return s}var qk=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=LX(t);this.userCode=`
+    `}};function pX(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],n=C.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:n,outSize:Math.ceil(t/n)})}return e}function An(r,e,t,n){let o=pX(r.shape),s=r;for(let a=0;a<o.length;a++){let{inSize:i,windowSize:l,outSize:u}=o[a],c,p;t==="mean"?c=a===0?new mx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new mx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new sv({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=n.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&n.disposeIntermediateTensorInfo(p)}return s}var iv=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let o=Ve(this.rank),s=mX(t);this.userCode=`
     void main() {
-      ${n} resRC = getOutputCoords();
+      ${o} resRC = getOutputCoords();
       setOutput(getA(${s}));
     }
-    `}};function LX(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var Kk=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Le(this.rank),s=Fk("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
+    `}};function mX(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(e);for(let o=0;o<r.length;o++)n[r[o]]=t[o];return n.join()}var av=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let o=Ve(this.rank),s=Kk("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
     void main() {
-      ${n} rc = getOutputCoords();
+      ${o} rc = getOutputCoords();
       vec4 result = vec4(0.);
       result[0] = ${u};
       if(${l}) {
         result[1] = ${u};
       }
       --${s[this.rank-1]};
-      if(++${s[this.rank-2]} < ${o[this.rank-2]}) {
+      if(++${s[this.rank-2]} < ${n[this.rank-2]}) {
         result[2] = ${u};
         if(${l}) {
           result[3] = ${u};
@@ -1215,40 +1215,40 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function yl(r,e,t){let o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kk(r.shape,e):new qk(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function CD(r,e,t,o){let n=e,s=r.shape.length,a=y.parseAxisParam(n,r.shape),i=a,l=N.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=yl(r,l,o),i=N.getInnerMostAxes(i.length,s)),N.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=N.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=N.expandShapeToKeepDim(p,a));let d=y.sizeFromShape(m),g=y.sizeFromShape(r.shape)/d,x=pe({inputs:{x:c},attrs:{shape:[g,d]},backend:o}),b=Gl(r.dtype),w=To(x,b,"sum",o),_=pe({inputs:{x:w},attrs:{shape:f},backend:o});return o.disposeIntermediateTensorInfo(x),o.disposeIntermediateTensorInfo(w),u&&o.disposeIntermediateTensorInfo(c),_}function Df(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return CD(n,s,a,t)}var ID={kernelName:is,backendName:"webgl",kernelFunc:Df};function Mt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let p=a.texData.get(n.dataId).values,m=Tu(p,n.shape,n.dtype,s,l);u=a.makeTensorInfo(l,n.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=yl(n,s,a);return u}var ND={kernelName:ps,backendName:"webgl",kernelFunc:Mt};var Xk=1e3;function Eu({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),w=x===b||x===1||b===1;y.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let k=(x>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);y.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let D=t?[x,p,f]:[x,f,p],T=o?[b,d,m]:[b,m,d],R=pe({inputs:{x:r},backend:n,attrs:{shape:D}}),O=pe({inputs:{x:e},backend:n,attrs:{shape:T}}),M=[R,O],G=Math.max(x,b),j=t?R.shape[1]:R.shape[2],U=s!=null,H=a!=null,q=l==="leakyrelu",X=l!=null?xl(l,!0):null,oe=U||H||q||X!=null,Y;if((f===1||d===1)&&j>Xk&&oe===!1){let J=R,ie=O;t&&(J=Mt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),M.push(J)),o&&(ie=Mt({inputs:{x:O},backend:n,attrs:{perm:[0,2,1]}}),M.push(ie));let ue=d!==1,ae=d===1,fe=J;ue&&(fe=pe({inputs:{x:J},backend:n,attrs:{shape:[G,j,1]}}),M.push(fe));let de=d===1?2:1,xe=ie;ae&&(xe=pe({inputs:{x:ie},backend:n,attrs:{shape:[G,1,j]}}),M.push(xe));let we=Uk({inputs:{a:fe,b:xe},backend:n});Y=Df({inputs:{x:we},backend:n,attrs:{axis:de,keepDims:!0}}),M.push(we)}else{let J=ir(r.dtype,e.dtype),ie=new Ef(D,T,[G,f,d],t,o,U,X,H,q),ue=[R,O];if(s!=null&&ue.push(s),H&&ue.push(a),q){let ae=n.makeTensorInfo([],"float32",y.createScalarValue(i,"float32"));ue.push(ae),M.push(ae)}Y=n.runWebGLProgram(ie,ue,J)}let re=pe({inputs:{x:Y},backend:n,attrs:{shape:k}});M.push(Y);for(let J of M)n.disposeIntermediateTensorInfo(J);return re}function zX(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return Eu({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var SD={kernelName:Ks,backendName:"webgl",kernelFunc:zX};var TD="return abs(x);";function BX(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=ox(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new vs(o.shape,TD):n=new mo(o.shape,TD),t.runWebGLProgram(n,[o],o.dtype)}var AD={kernelName:Os,backendName:"webgl",kernelFunc:BX};var VX=xr+`
+    `}};function kl(r,e,t){let n=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new av(r.shape,e):new iv(r.shape,e);return t.runWebGLProgram(n,[r],r.dtype)}function VD(r,e,t,n){let o=e,s=r.shape.length,a=x.parseAxisParam(o,r.shape),i=a,l=C.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=kl(r,l,n),i=C.getInnerMostAxes(i.length,s)),C.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=C.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=C.expandShapeToKeepDim(p,a));let d=x.sizeFromShape(m),g=x.sizeFromShape(r.shape)/d,y=ue({inputs:{x:c},attrs:{shape:[g,d]},backend:n}),b=Kl(r.dtype),w=An(y,b,"sum",n),_=ue({inputs:{x:w},attrs:{shape:f},backend:n});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(w),u&&n.disposeIntermediateTensorInfo(c),_}function Pu(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n;return VD(o,s,a,t)}var GD={kernelName:ss,backendName:"webgl",kernelFunc:Pu};function $t(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{perm:s}=n,a=t,i=o.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=o.shape[s[c]];let u;if(a.shouldExecuteOnCPU([o])){let p=a.texData.get(o.dataId).values,m=Fu(p,o.shape,o.dtype,s,l);u=a.makeTensorInfo(l,o.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=kl(o,s,a);return u}var WD={kernelName:ps,backendName:"webgl",kernelFunc:$t};var lv=1e3;function Mu({a:r,b:e,transposeA:t,transposeB:n,backend:o,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=n?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=n?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),y=x.sizeFromShape(h),b=x.sizeFromShape(g),w=y===b||y===1||b===1;x.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let I=(y>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);x.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${n} must match.`);let E=t?[y,p,f]:[y,f,p],$=n?[b,d,m]:[b,m,d],D=ue({inputs:{x:r},backend:o,attrs:{shape:E}}),O=ue({inputs:{x:e},backend:o,attrs:{shape:$}}),M=[D,O],G=Math.max(y,b),j=t?D.shape[1]:D.shape[2],U=s!=null,H=a!=null,q=l==="leakyrelu",X=l!=null?_l(l,!0):null,ne=U||H||q||X!=null,Y;if((f===1||d===1)&&j>lv&&ne===!1){let Q=D,ie=O;t&&(Q=$t({inputs:{x:D},backend:o,attrs:{perm:[0,2,1]}}),M.push(Q)),n&&(ie=$t({inputs:{x:O},backend:o,attrs:{perm:[0,2,1]}}),M.push(ie));let ce=d!==1,ae=d===1,fe=Q;ce&&(fe=ue({inputs:{x:Q},backend:o,attrs:{shape:[G,j,1]}}),M.push(fe));let de=d===1?2:1,xe=ie;ae&&(xe=ue({inputs:{x:ie},backend:o,attrs:{shape:[G,1,j]}}),M.push(xe));let we=Of({inputs:{a:fe,b:xe},backend:o});Y=Pu({inputs:{x:we},backend:o,attrs:{axis:de,keepDims:!0}}),M.push(we)}else{let Q=ar(r.dtype,e.dtype),ie=new Ff(E,$,[G,f,d],t,n,U,X,H,q),ce=[D,O];if(s!=null&&ce.push(s),H&&ce.push(a),q){let ae=o.makeTensorInfo([],"float32",x.createScalarValue(i,"float32"));ce.push(ae),M.push(ae)}Y=o.runWebGLProgram(ie,ce,Q)}let re=ue({inputs:{x:Y},backend:o,attrs:{shape:I}});M.push(Y);for(let Q of M)o.disposeIntermediateTensorInfo(Q);return re}function fX(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=n;return Mu({a:o,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var jD={kernelName:Zs,backendName:"webgl",kernelFunc:fX};var UD="return abs(x);";function dX(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=t.texData.get(n.dataId),a=ax(s.values);return t.makeTensorInfo(n.shape,n.dtype,a)}let o;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Cs(n.shape,UD):o=new hn(n.shape,UD),t.runWebGLProgram(o,[n],n.dtype)}var HD={kernelName:Ls,backendName:"webgl",kernelFunc:dX};var hX=yr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,GX=_e({opSnippet:VX}),ED={kernelName:yi,backendName:"webgl",kernelFunc:GX};var WX=xr+`
+`,gX=_e({opSnippet:hX}),qD={kernelName:_i,backendName:"webgl",kernelFunc:gX};var xX=yr+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,jX=_e({opSnippet:WX}),DD={kernelName:bi,backendName:"webgl",kernelFunc:jX};var $D="return a + b;",UX=nt({opSnippet:$D,packedOpSnippet:$D,supportsComplex:!0,cpuKernelImpl:A2}),RD={kernelName:Fo,backendName:"webgl",kernelFunc:UX};var Yk=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,yX=_e({opSnippet:xX}),KD={kernelName:ki,backendName:"webgl",kernelFunc:yX};var XD="return a + b;",bX=st({opSnippet:XD,packedOpSnippet:XD,supportsComplex:!0,cpuKernelImpl:H2}),YD={kernelName:On,backendName:"webgl",kernelFunc:bX};var uv=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`float v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
       void main() {
-        ${o.join(`
+        ${n.join(`
         `)}
 
-        float result = ${n};
+        float result = ${o};
         setOutput(result);
       }
-    `}};var Zk=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
+    `}};var cv=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let n=[];this.variableNames.forEach(s=>{n.push(`vec4 v${s} = get${s}AtOutCoords();`)});let o=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
       void main() {
-        ${o.join(`
+        ${n.join(`
         `)}
 
-        vec4 result = ${n};
+        vec4 result = ${o};
         setOutput(result);
       }
-    `}};function ux(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return jt({inputs:{x:o[0]},backend:t});if(o.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=ux({inputs:o.slice(0,l),backend:t}),c=ux({inputs:o.slice(l),backend:t});return ux({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>ir(l,u)),s=o.map(l=>l.shape),i=W().getBool("WEBGL_PACK")?new Zk(o[0].shape,s):new Yk(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var FD={kernelName:kn,backendName:"webgl",kernelFunc:ux};function HX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"all",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var OD={kernelName:mc,backendName:"webgl",kernelFunc:HX};function qX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"any",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var PD={kernelName:fc,backendName:"webgl",kernelFunc:qX};var Jk=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function fx(r){let{inputs:e,backend:t}=r,n=e;if(n.length===1)return jt({inputs:{x:n[0]},backend:t});if(n.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(n.length/2),u=fx({inputs:n.slice(0,l),backend:t}),c=fx({inputs:n.slice(l),backend:t});return fx({inputs:[u,c],backend:t})}let o=n.map(l=>l.dtype).reduce((l,u)=>ar(l,u)),s=n.map(l=>l.shape),i=W().getBool("WEBGL_PACK")?new cv(n[0].shape,s):new uv(n[0].shape,s);return t.runWebGLProgram(i,n,o)}var ZD={kernelName:wo,backendName:"webgl",kernelFunc:fx};function wX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=C.getAxesPermutation(u,i),p=o;c!=null&&(p=$t({inputs:{x:o},backend:t,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims("all",u,i);let[m,f]=C.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=ue({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=An(h,h.dtype,"all",t),y;if(a){let b=C.expandShapeToKeepDim(m,l);y=ue({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=ue({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var JD={kernelName:vi,backendName:"webgl",kernelFunc:wX};function _X(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=C.getAxesPermutation(u,i),p=o;c!=null&&(p=$t({inputs:{x:o},backend:t,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,i)),C.assertAxesAreInnerMostDims("any",u,i);let[m,f]=C.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=ue({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=An(h,h.dtype,"any",t),y;if(a){let b=C.expandShapeToKeepDim(m,l);y=ue({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=ue({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var QD={kernelName:Ci,backendName:"webgl",kernelFunc:_X};var pv=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:o,batchSize:s,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int outIdx = coords[1];
-        int inOffset = outIdx * ${n};
+        int inOffset = outIdx * ${o};
 
         int bestIndex = inOffset;
         float bestValue = getA(batch, bestIndex);
 
-        for (int i = 0; i < ${n}; i++) {
+        for (int i = 0; i < ${o}; i++) {
           int inIdx = ${l};
           float candidate = getA(batch, inIdx);
           if (candidate ${i} bestValue) {
@@ -1258,14 +1258,14 @@ return log(x + sqrt(x * x - 1.0));`,jX=_e({opSnippet:WX}),DD={kernelName:bi,back
         }
         setOutput(float(bestIndex));
       }
-    `}};var Qk=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=Wt("coords",l),p,m;if(a===1){m=l+1;let R=Le(m);p=`
-        ${R} sourceLocR = ${R}(${c.join()}, 0);
+    `}};var mv=class{constructor(e,t,n,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,x.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),o||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Ve(l),c=Wt("coords",l),p,m;if(a===1){m=l+1;let D=Ve(m);p=`
+        ${D} sourceLocR = ${D}(${c.join()}, 0);
         ++${c[l-1]};
-        ${R} sourceLocG = ${R}(${c.join()}, 0);
+        ${D} sourceLocG = ${D}(${c.join()}, 0);
         ++${c[l-2]};
-        ${R} sourceLocA = ${R}(${c.join()}, 0);
+        ${D} sourceLocA = ${D}(${c.join()}, 0);
         --${c[l-1]};
-        ${R} sourceLocB = ${R}(${c.join()}, 0);
+        ${D} sourceLocB = ${D}(${c.join()}, 0);
         --${c[l-2]};`}else m=l,p=`
         ${u} sourceLocR = coords;
         ++${c[l-1]};
@@ -1274,15 +1274,15 @@ return log(x + sqrt(x * x - 1.0));`,jX=_e({opSnippet:WX}),DD={kernelName:bi,back
         ${u} sourceLocA = coords;
         --${c[l-1]};
         ${u} sourceLocB = coords;
-        --${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(R=>"int "+R),g=Wt("sourceLocR",m-1).concat("inIdx.r"),x=Wt("sourceLocG",m-1).concat("inIdx.g"),b=Wt("sourceLocB",m-1).concat("inIdx.b"),w=Wt("sourceLocA",m-1).concat("inIdx.a"),_=o==="max"?"greaterThan":"lessThan",k=n?"":`
+        --${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(D=>"int "+D),g=Wt("sourceLocR",m-1).concat("inIdx.r"),y=Wt("sourceLocG",m-1).concat("inIdx.g"),b=Wt("sourceLocB",m-1).concat("inIdx.b"),w=Wt("sourceLocA",m-1).concat("inIdx.a"),_=n==="max"?"greaterThan":"lessThan",I=o?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
-                             getBestIndicesAChannel(${x.join()}),
+                             getBestIndicesAChannel(${y.join()}),
                              getBestIndicesAChannel(${b.join()}),
-                             getBestIndicesAChannel(${w.join()})));`,D=`vec4(
+                             getBestIndicesAChannel(${w.join()})));`,E=`vec4(
             getAChannel(${g.join()}),
-            hasNextCol ? getAChannel(${x.join()}) : 0.,
+            hasNextCol ? getAChannel(${y.join()}) : 0.,
             hasNextRow ? getAChannel(${b.join()}) : 0.,
-            hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,T=n?"":`
+            hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,$=o?"":`
       float getBestIndicesAChannel(${h.join()}) {
         return getChannel(getBestIndicesA(${f.join()}),
                                           vec2(${f.slice(-2).join()}));
@@ -1291,7 +1291,7 @@ return log(x + sqrt(x * x - 1.0));`,jX=_e({opSnippet:WX}),DD={kernelName:bi,back
         return getChannel(getA(${f.join()}),
                                vec2(${f.slice(-2).join()}));
       }
-      ${T}
+      ${$}
       void main() {
         ${u} coords = getOutputCoords();
         bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
@@ -1301,12 +1301,12 @@ return log(x + sqrt(x * x - 1.0));`,jX=_e({opSnippet:WX}),DD={kernelName:bi,back
           sourceLocB${d}, sourceLocA${d}) * ${t};
         ivec4 inIdx = srcIdx;
         vec4 bestIndex = vec4(inIdx);
-        vec4 bestValue = ${D};
+        vec4 bestValue = ${E};
 
         for (int i = 0; i < ${t}; i++) {
           inIdx = srcIdx;
-          ${k}
-          vec4 candidate = ${D};
+          ${I}
+          vec4 candidate = ${E};
           bvec4 nan = isnan(candidate);
           bvec4 replace = bvec4(
             vec4(${_}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
@@ -1320,23 +1320,23 @@ return log(x + sqrt(x * x - 1.0));`,jX=_e({opSnippet:WX}),DD={kernelName:bi,back
         }
         setOutput(bestIndex);
       }
-    `}};function MD(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new Jk(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=MD(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function LD(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=N.computeOptimalWindowSize(s),i=new Qk(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=LD(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function cx(r,e,t,o){let n=[t];if(N.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,n),l=y.sizeFromShape(i),u=pe({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=MD(r,u,o);s.push(c);let p=pe({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return LD(r,e,o)}function KX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Mt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=cx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var zD={kernelName:vn,backendName:"webgl",kernelFunc:KX};function XX(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Mt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=cx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var BD={kernelName:Ga,backendName:"webgl",kernelFunc:XX};var YX=xr+`
+    `}};function e$(r,e,t,n=null){let o=e.shape[0],s=e.shape[1];n!=null&&(o=n.shape[0],s=n.shape[1]);let a=C.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:o,outSize:Math.ceil(s/a)},l=new pv(i,t,n==null),u=[e];n!=null&&u.push(n);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=e$(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function t$(r,e,t,n=null){let o=n!=null?n.shape:e.shape,s=o[o.length-1],a=C.computeOptimalWindowSize(s),i=new mv(o,a,t,n==null),l=n==null?[e]:[e,n],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=t$(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function dx(r,e,t,n){let o=[t];if(C.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),o,e.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=C.computeOutAndReduceShapes(e.shape,o),l=x.sizeFromShape(i),u=ue({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=e$(r,u,n);s.push(c);let p=ue({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return t$(r,e,n)}function kX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=x.parseAxisParam(s,o.shape),i=C.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=$t({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=C.getInnerMostAxes(a.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=dx(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var r$={kernelName:_o,backendName:"webgl",kernelFunc:kX};function vX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s}=n,a=x.parseAxisParam(s,o.shape),i=C.getAxesPermutation(a,o.shape.length),l=o,u=[];i!=null&&(l=$t({inputs:{x:o},backend:t,attrs:{perm:i}}),u.push(l),a=C.getInnerMostAxes(a.length,l.shape.length)),C.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=dx(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var n$={kernelName:Ka,backendName:"webgl",kernelFunc:vX};var CX=yr+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,ZX=_e({opSnippet:YX}),VD={kernelName:wi,backendName:"webgl",kernelFunc:ZX};var JX=xr+"return log(x + sqrt(x * x + 1.0));",QX=_e({opSnippet:JX}),GD={kernelName:_i,backendName:"webgl",kernelFunc:QX};var e8=xr+`
+`,IX=_e({opSnippet:CX}),o$={kernelName:Ii,backendName:"webgl",kernelFunc:IX};var NX=yr+"return log(x + sqrt(x * x + 1.0));",SX=_e({opSnippet:NX}),s$={kernelName:Ni,backendName:"webgl",kernelFunc:SX};var TX=yr+`
   return atan(x);
-`,t8=_e({opSnippet:e8}),WD={kernelName:ki,backendName:"webgl",kernelFunc:t8};var r8=yD+`
+`,AX=_e({opSnippet:TX}),i$={kernelName:Si,backendName:"webgl",kernelFunc:AX};var EX=OD+`
   return atan(a, b);
-`,o8=`
+`,DX=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+bD+`
+  `+PD+`
   return result;
-`,n8=nt({opSnippet:r8,packedOpSnippet:o8}),jD={kernelName:Ci,backendName:"webgl",kernelFunc:n8};var s8=xr+`
+`,$X=st({opSnippet:EX,packedOpSnippet:DX}),a$={kernelName:Ai,backendName:"webgl",kernelFunc:$X};var RX=yr+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelName:vi,backendName:"webgl",kernelFunc:i8};var ii=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,FX=_e({opSnippet:RX}),l$={kernelName:Ti,backendName:"webgl",kernelFunc:FX};var ui=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),n){let D=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${l});
         const ivec2 pads = ivec2(${f}, ${d});
 
@@ -1378,16 +1378,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               // use the current value.
               float currMinMaxValue = mix(
                   value, minMaxValue, minMaxValueFound);
-              if (value ${R} currMinMaxValue) {
+              if (value ${D} currMinMaxValue) {
                 minMaxValue = value;
                 minMaxValueFound = 1.0;
-                minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};
+                minMaxPosition = ${o?s?g:y:`wR * ${m} + wC`};
               }
             }
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let w="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let k=Math.floor(a/4)*4,D=a%4,T=`
+      `;return}let w="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let I=Math.floor(a/4)*4,E=a%4,$=`
       if (${h}) {
         avgValue += dot(values, ones);
       } else {
@@ -1432,7 +1432,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
             continue;
           }
 
-          for (int wC = 0; wC < ${k}; wC += 4) {
+          for (int wC = 0; wC < ${I}; wC += 4) {
             int xC = xCCorner + wC * ${c};
 
             vec4 values = vec4(
@@ -1442,11 +1442,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               getValue(batch, xR, xC + 3 * ${c}, d)
             );
 
-            ${T}
+            ${$}
           }
 
-          int xC = xCCorner + ${k};
-          if (${D===1}) {
+          int xC = xCCorner + ${I};
+          if (${E===1}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               initializationValue,
@@ -1454,8 +1454,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               initializationValue
             );
 
-            ${T}
-          } else if (${D===2}) {
+            ${$}
+          } else if (${E===2}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               getValue(batch, xR, xC + ${c}, d),
@@ -1463,8 +1463,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               initializationValue
             );
 
-            ${T}
-          } else if (${D===3}) {
+            ${$}
+          } else if (${E===3}) {
             vec4 values = vec4(
               getValue(batch, xR, xC, d),
               getValue(batch, xR, xC + ${c}, d),
@@ -1472,15 +1472,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               initializationValue
             );
 
-            ${T}
+            ${$}
           }
         }
         setOutput(${_});
       }
-    `}},Du=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",_="0.0";if(w||(_="-1.0 / 1e-20"),o){let M=">=";this.userCode=`
+    `}},Lu=class{constructor(e,t,n,o=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,y=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",_="0.0";if(w||(_="-1.0 / 1e-20"),n){let M=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${l}, ${u});
-        const ivec3 pads = ivec3(${g}, ${x}, ${b});
+        const ivec3 pads = ivec3(${g}, ${y}, ${b});
 
         void main() {
           ivec5 coords = getOutputCoords();
@@ -1531,7 +1531,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
                 if (value ${M} currMinMaxValue) {
                   minMaxValue = value;
                   minMaxValueFound = 1.0;
-                  minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
+                  minMaxPosition = ${o?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
                       wR * ${h} + wC`};
                 }
               }
@@ -1539,16 +1539,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
           }
           setOutput(float(minMaxPosition));
         }
-      `;return}let k="max",D=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(D="avgValue / count");let T=Math.floor(a/4)*4,R=a%4,O=`
+      `;return}let I="max",E=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(E="avgValue / count");let $=Math.floor(a/4)*4,D=a%4,O=`
       if (${w}) {
         avgValue += dot(values, ones);
       } else {
-        minMaxValue = ${k}(values, minMaxValue);
+        minMaxValue = ${I}(values, minMaxValue);
       }
     `;this.userCode=`
       const ivec3 strides =
         ivec3(${i}, ${l}, ${u});
-      const ivec3 pads = ivec3(${g}, ${x}, ${b});
+      const ivec3 pads = ivec3(${g}, ${y}, ${b});
       const float initializationValue = ${_};
       const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
 
@@ -1594,7 +1594,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               continue;
             }
 
-            for (int wC = 0; wC < ${T}; wC += 4) {
+            for (int wC = 0; wC < ${$}; wC += 4) {
               int xC = xCCorner + wC * ${m};
 
               vec4 values = vec4(
@@ -1607,8 +1607,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               ${O}
             }
 
-            int xC = xCCorner + ${T};
-            if (${R===1}) {
+            int xC = xCCorner + ${$};
+            if (${D===1}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 initializationValue,
@@ -1617,7 +1617,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               );
 
               ${O}
-            } else if (${R===2}) {
+            } else if (${D===2}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 getValue(batch, xD, xR, xC + ${m}, ch),
@@ -1626,7 +1626,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               );
 
               ${O}
-            } else if (${R===3}) {
+            } else if (${D===3}) {
               vec4 values = vec4(
                 getValue(batch, xD, xR, xC, ch),
                 getValue(batch, xD, xR, xC + ${m}, ch),
@@ -1637,10 +1637,10 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               ${O}
             }
           }
-          setOutput(${D});
+          setOutput(${E});
         }
       }
-    `}};function a8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;_s(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new ii(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var HD={kernelName:Cn,backendName:"webgl",kernelFunc:a8};function l8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new Du(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var qD={kernelName:Wa,backendName:"webgl",kernelFunc:l8};var ev=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`
+    `}};function OX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;ks(o,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;x.assert(C.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=C.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:o},backend:t});let p=new ui(c,"avg",!1);return t.runWebGLProgram(p,[o],"float32")}var u$={kernelName:ko,backendName:"webgl",kernelFunc:OX};function PX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=n,c=[1,1,1],p=C.computePool3DInfo(o.shape,s,a,c,i,l,u),m=new Lu(p,"avg",!1);return t.runWebGLProgram(m,[o],"float32")}var c$={kernelName:Xa,backendName:"webgl",kernelFunc:PX};var fv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${c}, ${p});
       const float avgMultiplier = float(${m});
 
@@ -1658,7 +1658,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         float dotProd = 0.0;
         for (int wR = 0; wR < ${l};
             wR += ${a}) {
-          float dyR = float(dyRCorner + wR) / ${n}.0;
+          float dyR = float(dyRCorner + wR) / ${o}.0;
 
           if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
@@ -1682,9 +1682,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},tv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`
+    `}},dv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,y=1/(t*n*o);this.userCode=`
       const ivec3 pads = ivec3(${d}, ${h}, ${g});
-      const float avgMultiplier = float(${x});
+      const float avgMultiplier = float(${y});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -1738,7 +1738,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function u8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new tv(m);return t.runWebGLProgram(f,[n],a.dtype)}var KD={kernelName:hc,backendName:"webgl",kernelFunc:u8};function c8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;_s([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new ev(c);return t.runWebGLProgram(p,[n],a.dtype)}var XD={kernelName:dc,backendName:"webgl",kernelFunc:c8};function p8(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Eu({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var YD={kernelName:In,backendName:"webgl",kernelFunc:p8};var rv=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function MX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=C.computePool3DInfo(a.shape,i,l,p,u,c),f=new dv(m);return t.runWebGLProgram(f,[o],a.dtype)}var p$={kernelName:xc,backendName:"webgl",kernelFunc:MX};function LX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s;ks([o,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=n,c=C.computePool2DInfo(a.shape,i,l,1,u),p=new fv(c);return t.runWebGLProgram(p,[o],a.dtype)}var m$={kernelName:gc,backendName:"webgl",kernelFunc:LX};function zX(r){let{inputs:e,backend:t,attrs:n}=r,{a:o,b:s}=e,{transposeA:a,transposeB:i}=n;return Mu({a:o,b:s,transposeA:a,transposeB:i,backend:t})}var f$={kernelName:vo,backendName:"webgl",kernelFunc:zX};var hv=class{constructor(e,t,n,o,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="0.0";o!=null&&(C.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -1748,7 +1748,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         float inv = scale * inversesqrt(variance + float(${a}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}};var ov=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};var gv=class{constructor(e,t,n,o,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],C.assertAndGetBroadcastShape(e,t),C.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";o!=null&&(C.assertAndGetBroadcastShape(e,o),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(C.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${l};
@@ -1761,36 +1761,36 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}};var m8=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),y.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),y.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=W().getBool("WEBGL_PACK_NORMALIZATION")?new ov(o.shape,n.shape,s.shape,c,p,l):new rv(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},ZD={kernelName:Pn,backendName:"webgl",kernelFunc:m8};var nv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=`uniform int start[${this.rank}];`,n=f8(this.rank),s,a=e.map((i,l)=>`sourceLoc.${sv[l]} = start[${l}] + coords.${sv[l]};`);s=`
+    `}};var BX=({inputs:r,backend:e,attrs:t})=>{let{x:n,mean:o,variance:s,offset:a,scale:i}=r;x.assert(o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),x.assert(a==null||o.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),x.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[n,o,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=W().getBool("WEBGL_PACK_NORMALIZATION")?new gv(n.shape,o.shape,s.shape,c,p,l):new hv(n.shape,o.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},d$={kernelName:Fo,backendName:"webgl",kernelFunc:BX};var xv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Ve(this.rank),n=`uniform int start[${this.rank}];`,o=VX(this.rank),s,a=e.map((i,l)=>`sourceLoc.${yv[l]} = start[${l}] + coords.${yv[l]};`);s=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${a.join(`
 `)}
       `,this.userCode=`
-      ${o}
+      ${n}
       void main() {
         ${s}
-        setOutput(getSource(${n}));
+        setOutput(getSource(${o}));
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},sv=["x","y","z","w","u","v"];function f8(r){if(r===1)return"sourceLoc";if(r<=6)return sv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var iv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=Wt("coords",this.rank),n=Wt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},yv=["x","y","z","w","u","v"];function VX(r){if(r===1)return"sourceLoc";if(r<=6)return yv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var bv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Ve(this.rank),n=Wt("coords",this.rank),o=Wt("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${o.slice(-2).join()})`,a=`getChannel(getSource(${o.join()}), ${s})`,i=`
       result.x = ${a};
-      if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
-        ++${n[this.rank-1]};
+      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
+        ++${o[this.rank-1]};
         result.y = ${a};
-        --${n[this.rank-1]};
+        --${o[this.rank-1]};
       }
     `,l=this.rank===1?"":`
-      --${o[this.rank-1]};
-      if (++${o[this.rank-2]} < ${e[this.rank-2]}) {
-        ++${n[this.rank-2]};
+      --${n[this.rank-1]};
+      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
+        ++${o[this.rank-2]};
         result.z = ${a};
-        if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
-          ++${n[this.rank-1]};
+        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
+          ++${o[this.rank-1]};
           result.w = ${a};
         }
       }
     `,u=this.rank<=4?`sourceLoc = coords +
-            ${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${o[p]} + start[${p}];`).join(`
+            ${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${o[p]} = ${n[p]} + start[${p}];`).join(`
 `);this.userCode=`
       uniform int start[${this.rank}];
       void main() {
@@ -1802,7 +1802,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         ${l}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function d8(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=er.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function Na(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=er.parseSliceParams(n,s,a);if(er.assertParamsValid(n,i,l),y.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=X2(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=er.isSliceContinous(n.shape,i,l);if(u||!c){let p=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iv(l):new nv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),d8(n,i,l,t)}var JD={kernelName:js,backendName:"webgl",kernelFunc:Na};var h8=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,w)=>b*w),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=[],d=pe({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Mt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Na({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},QD={kernelName:ja,backendName:"webgl",kernelFunc:h8};function g8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=rx(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var e$={kernelName:gc,backendName:"webgl",kernelFunc:g8};var x8="return float(a != b);",av=nt({opSnippet:x8,dtype:"bool"}),t$={kernelName:Wi,backendName:"webgl",kernelFunc:av};function Sa(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.real},backend:t})}var r$={kernelName:Oc,backendName:"webgl",kernelFunc:Sa};var y8="return float(int(x));";function o$(r,e){let t=new mo(r.shape,y8),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function lv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return jt({inputs:{x:n},backend:t});let a=ht(n.shape),i=lv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=fo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=Sa({inputs:{input:n},backend:t}),i=lv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=jt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return o$(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",y.getTypedArrayFromDType("bool",1)),l=av({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var n$={kernelName:Ho,backendName:"webgl",kernelFunc:lv};var s$="return ceil(x);",b8=_e({opSnippet:s$,packedOpSnippet:s$,cpuKernelImpl:D2}),i$={kernelName:Nn,backendName:"webgl",kernelFunc:b8};var uv=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function GX(r,e,t,n){let o=n.texData.get(r.dataId),s=n.makeTensorInfo(t,r.dtype),a=n.texData.get(s.dataId);Object.assign(a,o),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=rr.computeFlatOffset(e,x.computeStrides(r.shape));o.slice&&(i+=o.slice.flatOffset),a.slice={flatOffset:i,origDataId:o.slice&&o.slice.origDataId||r.dataId};let l=n.dataRefCount.get(a.slice.origDataId)||1;return n.dataRefCount.set(a.slice.origDataId,l+1),s}function $a(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,size:a}=n,[i,l]=rr.parseSliceParams(o,s,a);if(rr.assertParamsValid(o,i,l),x.sizeFromShape(l)===0)return t.makeTensorInfo(l,o.dtype,[]);if(t.shouldExecuteOnCPU([o])||o.dtype==="string"){let p=t.texData.get(o.dataId),m=mD(p.values,i,l,o.shape,o.dtype);return t.makeTensorInfo(l,o.dtype,m)}let{isPacked:u}=t.texData.get(o.dataId),c=rr.isSliceContinous(o.shape,i,l);if(u||!c){let p=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bv(l):new xv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[o],o.dtype,m)}return t.uploadToGPU(o.dataId),GX(o,i,l,t)}var h$={kernelName:qs,backendName:"webgl",kernelFunc:$a};var WX=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,crops:a}=n;x.assert(o.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,w)=>b*w),l=C.getReshaped(o.shape,s,i),u=C.getPermuted(l.length,s.length),c=C.getReshapedPermuted(o.shape,s,i),p=C.getSliceBeginCoords(a,s.length),m=C.getSliceSize(c,a,s.length),f=[],d=ue({inputs:{x:o},backend:t,attrs:{shape:l}}),h=$t({inputs:{x:d},backend:t,attrs:{perm:u}}),g=ue({inputs:{x:h},backend:t,attrs:{shape:c}}),y=$a({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),y},g$={kernelName:Ya,backendName:"webgl",kernelFunc:WX};function jX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a}=n,i=t.readSync(o.dataId),l=t.readSync(s.dataId),u=ix(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var x$={kernelName:yc,backendName:"webgl",kernelFunc:jX};var UX="return float(a != b);",wv=st({opSnippet:UX,dtype:"bool"}),y$={kernelName:Ki,backendName:"webgl",kernelFunc:wv};function Ra(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return jt({inputs:{x:o.complexTensorInfos.real},backend:t})}var b$={kernelName:Lc,backendName:"webgl",kernelFunc:Ra};var HX="return float(int(x));";function w$(r,e){let t=new hn(r.shape,HX),n=e.runWebGLProgram(t,[r],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function _v(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dtype:s}=n;if(s==="complex64"){if(o.dtype==="complex64")return jt({inputs:{x:o},backend:t});let a=ht(o.shape),i=_v({inputs:{x:o},backend:t,attrs:{dtype:"float32"}}),l=gn({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(o.dtype==="complex64"){let a=Ra({inputs:{input:o},backend:t}),i=_v({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!x.hasEncodingLoss(o.dtype,s)){let a=jt({inputs:{x:o},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return w$(o,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",x.getTypedArrayFromDType("bool",1)),l=wv({inputs:{a:o,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${o.dtype} to ${s}`)}var _$={kernelName:qn,backendName:"webgl",kernelFunc:_v};var k$="return ceil(x);",qX=_e({opSnippet:k$,packedOpSnippet:k$,cpuKernelImpl:K2}),v$={kernelName:Co,backendName:"webgl",kernelFunc:qX};var kv=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1815,7 +1815,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var cv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,o)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(o,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};var vv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
       uniform float minVal;
       uniform float maxVal;
 
@@ -1829,7 +1829,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function w8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;W().getBool("WEBGL_PACK_CLIP")?i=new cv(n.shape):i=new uv(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var a$={kernelName:qo,backendName:"webgl",kernelFunc:w8};var pv=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e,t){return(n,o)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(o,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(o,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function KX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{clipValueMin:s,clipValueMax:a}=n,i;W().getBool("WEBGL_PACK_CLIP")?i=new vv(o.shape):i=new kv(o.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[o],o.dtype,l)}var C$={kernelName:Kn,backendName:"webgl",kernelFunc:KX};var Cv=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -1842,27 +1842,27 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function l$(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function _8(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new pv(o.shape),a=[l$(o,n.complexTensorInfos.real),l$(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var u$={kernelName:Ua,backendName:"webgl",kernelFunc:_8};var mv=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`
+    `}};function I$(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function XX(r){let{inputs:e,backend:t}=r,{x:n}=e,o=t.texData.get(n.dataId),s=new Cv(n.shape),a=[I$(n,o.complexTensorInfos.real),I$(n,o.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var N$={kernelName:Za,backendName:"webgl",kernelFunc:XX};var Iv=class{constructor(e){this.outputShape=[],this.outputShape=C.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let o=t.length,s=t[t.length-1];n.push(`else setOutput(getT${o}(yR, yC-${s}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
         int yC = coords.y;
 
-        ${o.join(`
+        ${n.join(`
         `)}
       }
-    `}};var fv=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=Le(n),a=Wt("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
+    `}};var Nv=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=C.computeOutShape(e,t);let n=this.outputShape,o=n.length,s=Ve(o),a=Wt("coords",o),i=["x","y","z","w","u","v"].slice(0,o);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
         return getChannel(
             getT0(${p}), vec2(${c.join()}));
         }`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
         if (${u} < ${l[h]}  && ${u} >= ${l[h-1]}) {
           return getChannel(
-            getT${h}(${px(i,u,g)}),
-            vec2(${px(c,u,g)}));
+            getT${h}(${hx(i,u,g)}),
+            vec2(${hx(c,u,g)}));
         }`}let f=l.length,d=l[l.length-1];m+=`
         return getChannel(
-          getT${f}(${px(i,u,d)}),
-          vec2(${px(c,u,d)}));`,this.userCode=`
+          getT${f}(${hx(i,u,d)}),
+          vec2(${hx(c,u,d)}));`,this.userCode=`
       float getValue(${i.map(h=>"int "+h)}) {
         ${m}
       }
@@ -1871,34 +1871,34 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         ${s} coords = getOutputCoords();
         vec4 result = vec4(getValue(${a}), 0., 0., 0.);
 
-        ${a[n-1]} = ${a[n-1]} + 1;
-        if (${a[n-1]} < ${o[n-1]}) {
+        ${a[o-1]} = ${a[o-1]} + 1;
+        if (${a[o-1]} < ${n[o-1]}) {
           result.g = getValue(${a});
         }
 
-        ${a[n-2]} = ${a[n-2]} + 1;
-        if (${a[n-2]} < ${o[n-2]}) {
+        ${a[o-2]} = ${a[o-2]} + 1;
+        if (${a[o-2]} < ${n[o-2]}) {
           result.a = getValue(${a});
         }
 
-        ${a[n-1]} = ${a[n-1]} - 1;
-        if (${a[n-2]} < ${o[n-2]} &&
-            ${a[n-1]} < ${o[n-1]}) {
+        ${a[o-1]} = ${a[o-1]} - 1;
+        if (${a[o-2]} < ${n[o-2]} &&
+            ${a[o-1]} < ${n[o-1]}) {
           result.b = getValue(${a});
         }
         setOutput(result);
       }
-    `}};function px(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function $u(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var c$={kernelName:Tc,backendName:"webgl",kernelFunc:$u};function Ru(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>Sa({inputs:{input:d},backend:t})),c=r.map(d=>$u({inputs:{input:d},backend:t})),p=Ru(u,e,t),m=Ru(c,e,t),f=fo({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(o==="string"){let{tensors2D:u,outShape:c}=p$(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=$2(p,c,o,m),d=N.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=Ru(r.slice(0,u),e,t),p=Ru(r.slice(u),e,t),m=Ru([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new fv(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=p$(r,e,t),a=new mv(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=pe({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function p$(r,e,t){let o=N.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function dv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>y.sizeFromShape(u.shape)>0);if(i.length===1)return jt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return N.assertParamsConsistent(l,s),Ru(i,s,t)}var m$={kernelName:Ps,backendName:"webgl",kernelFunc:dv};var $f=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",x=g?1:2,b=g?2:3,w=g?3:1,_="",k="";o&&(n?_=`float activation(float a) {
+    `}};function hx(r,e,t){let n=r.indexOf(e);return r.map((s,a)=>a===n?`${s} - ${t}`:s).join()}function zu(r){let{inputs:e,backend:t}=r,{input:n}=e,o=t.texData.get(n.dataId);return jt({inputs:{x:o.complexTensorInfos.imag},backend:t})}var S$={kernelName:Dc,backendName:"webgl",kernelFunc:zu};function Bu(r,e,t){let n=r[0].dtype;if(n==="complex64"){let u=r.map(d=>Ra({inputs:{input:d},backend:t})),c=r.map(d=>zu({inputs:{input:d},backend:t})),p=Bu(u,e,t),m=Bu(c,e,t),f=gn({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(n==="string"){let{tensors2D:u,outShape:c}=T$(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=X2(p,c,n,m),d=C.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,n,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=Bu(r.slice(0,u),e,t),p=Bu(r.slice(u),e,t),m=Bu([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new Nv(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,n)}let{tensors2D:o,outShape:s}=T$(r,e,t),a=new Iv(o.map(u=>u.shape)),i=t.runWebGLProgram(a,o,n);o.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=ue({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function T$(r,e,t){let n=C.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>ue({inputs:{x:s},attrs:{shape:[-1,x.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:n}}function Sv(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n,s=x.parseAxisParam(o,e[0].shape)[0],a=C.computeOutShape(e.map(u=>u.shape),s);if(x.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>x.sizeFromShape(u.shape)>0);if(i.length===1)return jt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return C.assertParamsConsistent(l,s),Bu(i,s,t)}var A$={kernelName:zs,backendName:"webgl",kernelFunc:Sv};var Pf=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",y=g?1:2,b=g?2:3,w=g?3:1,_="",I="";n&&(o?_=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
-          ${o}
+          ${n}
         }`:s?_=`float activation(float a) {
           float b = getLeakyreluAlphaAtOutCoords();
-          ${o}
+          ${n}
         }`:_=`
           float activation(float x) {
-            ${o}
+            ${n}
           }
-        `,k="result = activation(result);");let D=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+        `,I="result = activation(result);");let E=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
       ${_}
 
       const ivec2 strides = ivec2(${l}, ${u});
@@ -1910,7 +1910,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         int d2 = coords[${w}];
 
         ivec2 xRCCorner =
-            ivec2(coords[${x}], coords[${b}]) * strides - pads;
+            ivec2(coords[${y}], coords[${b}]) * strides - pads;
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
 
@@ -2018,13 +2018,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
 
         float result = dotProd;
-        ${D}
-        ${k}
+        ${E}
+        ${I}
         setOutput(result);
       }
-    `}},hv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
+    `}},Tv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,o=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${s}, ${a}, ${i});
-      const ivec3 pads = ivec3(${t}, ${o}, ${n});
+      const ivec3 pads = ivec3(${t}, ${n}, ${o});
 
       void main() {
         ivec5 coords = getOutputCoords();
@@ -2110,9 +2110,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};var gv=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=Rt(),x=m==="channelsLast",b=x?0:1,w=x?1:2,_="";for(let k=0;k<=1;k++)for(let D=0;D<=1;D++)_+=`
-          blockIndex = rc.y + ${D};
-          pos = rc.x + ${k};
+    `}};var Av=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:o,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=n,{left:f,top:d}=l,h=s*o,g=Ot(),y=m==="channelsLast",b=y?0:1,w=y?1:2,_="";for(let I=0;I<=1;I++)for(let E=0;E<=1;E++)_+=`
+          blockIndex = rc.y + ${E};
+          pos = rc.x + ${I};
 
           if(blockIndex < ${e[1]} && pos < ${e[0]}) {
             offsetY = int(blockIndex / (${u})) * ${i} - ${d};
@@ -2127,14 +2127,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
                 ch = int(mod(float(pos), ${s}.));
 
-                if (${x}) {
+                if (${y}) {
                   innerDims = vec2(d1, ch);
-                  result[${k*2+D}] = getChannel(
+                  result[${I*2+E}] = getChannel(
                     getA(d0, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 } else {
                   innerDims = vec2(d0, d1);
-                  result[${k*2+D}] = getChannel(
+                  result[${I*2+E}] = getChannel(
                     getA(ch, int(innerDims.x),
                     int(innerDims.y)), innerDims);
                 }
@@ -2154,7 +2154,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
         ${g.output} = result;
       }
-    `}};function mx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,x=[],b=(p===1||m===1)&&c>Xk,w=l[2]%2!=0&&!!u.isPacked;if(b||!W().getBool("WEBGL_LAZILY_UNPACK")||!W().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],k=pe({inputs:{x:r},backend:o,attrs:{shape:[1,_,t.inChannels]}}),D=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),T=Eu({a:k,b:D,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=pe({inputs:{x:T},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(D),x.push(T)}else{let _=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),k={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},D=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(hl(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let T=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push(T);let R=Eu({a:k,b:T,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),O=o.texData.get(R.dataId);y.assert(O.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=D,O.shape=t.outShape,g=jt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}for(let _ of x)o.disposeIntermediateTensorInfo(_);return g}function fx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,x=[h,g],b=!0,w=!1,_=[],k=pe({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),D=pe({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});_.push(k),_.push(D);let T=new gv(x,k.shape,t),R=o.runWebGLProgram(T,[k],"float32"),O=pe({inputs:{x:R},backend:o,attrs:{shape:[1,x[0],x[1]]}});_.push(R),_.push(O);let M=n!=null,G=s!=null,j=i==="leakyrelu",U=i?xl(i,!0):null,H=new Ef(O.shape,D.shape,[1,g,t.outChannels],b,w,M,U,G,j),q=[O,D];if(n&&q.push(n),G&&q.push(s),j){let re=o.makeTensorInfo([],"float32",y.createScalarValue(a,"float32"));q.push(re),_.push(re)}let X=o.runWebGLProgram(H,q,"float32"),oe=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],Y=pe({inputs:{x:X},backend:o,attrs:{shape:oe}});_.push(X);for(let re of _)o.disposeIntermediateTensorInfo(re);return Y}function k8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=mx({x:n,filter:s,convInfo:m,backend:t});else if(W().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=fx({x:n,filter:s,convInfo:m,backend:t});else{let h=new $f(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=pe({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var f$={kernelName:Sn,backendName:"webgl",kernelFunc:k8};var xv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function gx({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=n.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,y=[],b=(p===1||m===1)&&c>lv,w=l[2]%2!=0&&!!u.isPacked;if(b||!W().getBool("WEBGL_LAZILY_UNPACK")||!W().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],I=ue({inputs:{x:r},backend:n,attrs:{shape:[1,_,t.inChannels]}}),E=ue({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Mu({a:I,b:E,transposeA:d,transposeB:h,backend:n,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=ue({inputs:{x:$},backend:n,attrs:{shape:t.outShape}}),y.push(I),y.push(E),y.push($)}else{let _=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),I={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},E=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,x.assert(bl(u.shape,I.shape),()=>`packed reshape ${u.shape} to ${I.shape} isn't free`);let $=ue({inputs:{x:e},backend:n,attrs:{shape:[1,t.inChannels,t.outChannels]}});y.push($);let D=Mu({a:I,b:$,backend:n,transposeA:d,transposeB:h,bias:o,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),O=n.texData.get(D.dataId);x.assert(O.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=E,O.shape=t.outShape,g=jt({inputs:{x:D},backend:n}),g.shape=t.outShape,y.push(D)}for(let _ of y)n.disposeIntermediateTensorInfo(_);return g}function xx({x:r,filter:e,convInfo:t,backend:n,bias:o=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,y=[h,g],b=!0,w=!1,_=[],I=ue({inputs:{x:r},backend:n,attrs:{shape:r.shape.slice(1)}}),E=ue({inputs:{x:e},backend:n,attrs:{shape:[1,h,x.sizeFromShape(e.shape)/h]}});_.push(I),_.push(E);let $=new Av(y,I.shape,t),D=n.runWebGLProgram($,[I],"float32"),O=ue({inputs:{x:D},backend:n,attrs:{shape:[1,y[0],y[1]]}});_.push(D),_.push(O);let M=o!=null,G=s!=null,j=i==="leakyrelu",U=i?_l(i,!0):null,H=new Ff(O.shape,E.shape,[1,g,t.outChannels],b,w,M,U,G,j),q=[O,E];if(o&&q.push(o),G&&q.push(s),j){let re=n.makeTensorInfo([],"float32",x.createScalarValue(a,"float32"));q.push(re),_.push(re)}let X=n.runWebGLProgram(H,q,"float32"),ne=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],Y=ue({inputs:{x:X},backend:n,attrs:{shape:ne}});_.push(X);for(let re of _)n.disposeIntermediateTensorInfo(re);return Y}function YX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=C.convertConv2DDataFormat(l),m=C.computeConv2DInfo(o.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=gx({x:o,filter:s,convInfo:m,backend:t});else if(W().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)f=xx({x:o,filter:s,convInfo:m,backend:t});else{let h=new Pf(m);f=t.runWebGLProgram(h,[o,s],"float32")}let d=ue({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var E$={kernelName:Io,backendName:"webgl",kernelFunc:YX};var Ev=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2168,14 +2168,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
         for (int b = 0; b < ${e.batchSize}; b++) {
           for (int yR = 0; yR < ${e.outHeight}; yR++) {
-            int xR = wR + yR * ${t} - ${n};
+            int xR = wR + yR * ${t} - ${o};
 
             if (xR < 0 || xR >= ${e.inHeight}) {
               continue;
             }
 
             for (int yC = 0; yC < ${e.outWidth}; yC++) {
-              int xC = wC + yC * ${o} - ${s};
+              int xC = wC + yC * ${n} - ${s};
 
               if (xC < 0 || xC >= ${e.inWidth}) {
                 continue;
@@ -2196,7 +2196,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},yv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
+    `}},Dv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=n-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${l});
 
       void main() {
@@ -2212,7 +2212,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
         for (int wR = 0; wR < ${t}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${n}.0;
+          float dyR = float(dyRCorner + wR) / ${o}.0;
 
           if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
@@ -2221,7 +2221,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
           int wRPerm = ${t} - 1 - wR;
 
-          for (int wC = 0; wC < ${o}; wC++) {
+          for (int wC = 0; wC < ${n}; wC++) {
             float dyC = float(dyCCorner + wC) / ${s}.0;
 
             if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -2230,7 +2230,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
             }
             int idyC = int(dyC);
 
-            int wCPerm = ${o} - 1 - wC;
+            int wCPerm = ${n} - 1 - wC;
 
             for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
 
@@ -2249,7 +2249,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},bv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},$v=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2269,14 +2269,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
             }
 
             for (int yR = 0; yR < ${e.outHeight}; yR++) {
-              int xR = wR + yR * ${o} - ${a};
+              int xR = wR + yR * ${n} - ${a};
 
               if (xR < 0 || xR >= ${e.inHeight}) {
                 continue;
               }
 
               for (int yC = 0; yC < ${e.outWidth}; yC++) {
-                int xC = wC + yC * ${n} - ${i};
+                int xC = wC + yC * ${o} - ${i};
 
                 if (xC < 0 || xC >= ${e.inWidth}) {
                   continue;
@@ -2291,7 +2291,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},wv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
+    `}},Rv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,o=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=n-1-e.padInfo.top,c=o-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${l}, ${u}, ${c});
 
       void main() {
@@ -2316,7 +2316,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
           int wFPerm = ${t} - 1 - wF;
 
-          for (int wR = 0; wR < ${o}; wR++) {
+          for (int wR = 0; wR < ${n}; wR++) {
             float dyR = float(dyRCorner + wR) / ${a}.0;
 
             if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
@@ -2325,9 +2325,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
             }
             int idyR = int(dyR);
 
-            int wRPerm = ${o} - 1 - wR;
+            int wRPerm = ${n} - 1 - wR;
 
-            for (int wC = 0; wC < ${n}; wC++) {
+            for (int wC = 0; wC < ${o}; wC++) {
               float dyC = float(dyCCorner + wC) / ${i}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -2336,7 +2336,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
               }
               int idyC = int(dyC);
 
-              int wCPerm = ${n} - 1 - wC;
+              int wCPerm = ${o} - 1 - wC;
 
               for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
                 float xValue = getDy(batch, idyF, idyR, idyC, d2);
@@ -2348,12 +2348,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function v8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new xv(m);return t.runWebGLProgram(f,[n,s],"float32")}var d$={kernelName:yc,backendName:"webgl",kernelFunc:v8};function C8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new yv(m);return t.runWebGLProgram(f,[n,s],"float32")}var h$={kernelName:Tn,backendName:"webgl",kernelFunc:C8};function I8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new hv(u);return t.runWebGLProgram(c,[n,s],"float32")}var g$={kernelName:Ha,backendName:"webgl",kernelFunc:I8};function N8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=N.computeConv3DInfo(n.shape,l,a,1,i),c=new bv(u);return t.runWebGLProgram(c,[n,s],"float32")}var x$={kernelName:bc,backendName:"webgl",kernelFunc:N8};function S8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new wv(u);return t.runWebGLProgram(c,[n,s],"float32")}var y$={kernelName:wc,backendName:"webgl",kernelFunc:S8};var T8=ix+`
+    `}};function ZX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=n,p=C.convertConv2DDataFormat(l),m=C.computeConv2DInfo(o.shape,c,a,1,i,u,!1,p),f=new Ev(m);return t.runWebGLProgram(f,[o,s],"float32")}var D$={kernelName:wc,backendName:"webgl",kernelFunc:ZX};function JX(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=n,p=C.convertConv2DDataFormat(u),m=C.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new Dv(m);return t.runWebGLProgram(f,[o,s],"float32")}var $$={kernelName:No,backendName:"webgl",kernelFunc:JX};function QX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=C.computeConv3DInfo(o.shape,s.shape,a,l,i),c=new Tv(u);return t.runWebGLProgram(c,[o,s],"float32")}var R$={kernelName:Ja,backendName:"webgl",kernelFunc:QX};function e7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,pad:i,filterShape:l}=n,u=C.computeConv3DInfo(o.shape,l,a,1,i),c=new $v(u);return t.runWebGLProgram(c,[o,s],"float32")}var F$={kernelName:_c,backendName:"webgl",kernelFunc:e7};function t7(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{pad:a,strides:i,inputShape:l}=n,u=C.computeConv3DInfo(l,s.shape,i,1,a),c=new Rv(u);return t.runWebGLProgram(c,[o,s],"float32")}var O$={kernelName:kc,backendName:"webgl",kernelFunc:t7};var r7=cx+`
   return cos(x);
-`,A8=_e({opSnippet:T8}),b$={kernelName:An,backendName:"webgl",kernelFunc:A8};var E8=`
+`,n7=_e({opSnippet:r7}),P$={kernelName:So,backendName:"webgl",kernelFunc:n7};var o7=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,D8=_e({opSnippet:E8}),w$={kernelName:Ii,backendName:"webgl",kernelFunc:D8};var _v=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,x,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,_,k]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
+`,s7=_e({opSnippet:o7}),M$={kernelName:Ei,backendName:"webgl",kernelFunc:s7};var Fv=class{constructor(e,t,n,o,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=n;this.outputShape=[c,p,m,u];let f=o==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,y,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,_,I]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
       const float height_ratio = float(${g});
       const float width_ratio = float(${w});
       void main() {
@@ -2375,7 +2375,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
           return;
         }
 
-        float height_scale = ${x};
+        float height_scale = ${y};
         float width_scale = ${_};
 
         float in_y = ${b};
@@ -2383,7 +2383,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
           setOutput(float(${s}));
           return;
         }
-        float in_x = ${k};
+        float in_x = ${I};
         if( in_x < 0.0 || in_x > ${h} ) {
           setOutput(float(${s}));
           return;
@@ -2414,21 +2414,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
           setOutput(newValue);
         }
       }
-    `}};var $8=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new _v(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},_$={kernelName:Ni,backendName:"webgl",kernelFunc:$8};var dx=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${k$(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=`
+    `}};var i7=r=>{let{inputs:e,backend:t,attrs:n}=r,{image:o,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=n,c=new Fv(o.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[o,s,a],"float32")},L$={kernelName:Di,backendName:"webgl",kernelFunc:i7};var yx=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let o=e.length,s=t?"0.0":`getX(${z$(o,"coords")})`,a=e[e.length-1],i="",l="";t?(i=n?`end != ${a-1}`:"end != 0",l=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",l=n?"end + pow2":"end - pow2"),this.userCode=`
       uniform float index;
       void main() {
-        ${Le(n)} coords = getOutputCoords();
-        int end = ${v$(n,"coords")};
+        ${Ve(o)} coords = getOutputCoords();
+        int end = ${B$(o,"coords")};
         float val = ${s};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${l};
-          ${v$(n,"coords")} = idx;
-          val += getX(${k$(n,"coords")});
+          ${B$(o,"coords")} = idx;
+          val += getX(${z$(o,"coords")});
         }
         setOutput(val);
       }
-    `}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function k$(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function v$(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function R8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=N.getAxesPermutation([s],l),c=n;u!=null&&(c=Mt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=jt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new dx(c.shape,!1,i),g=h.getCustomSetupFunc(d),x=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(x)}if(a){let d=new dx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=Mt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var C$={kernelName:En,backendName:"webgl",kernelFunc:R8};function F8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=rx(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=E2(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var I$={kernelName:_c,backendName:"webgl",kernelFunc:F8};var kv=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function z$(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function B$(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function a7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,exclusive:a,reverse:i}=n,l=o.shape.length,u=C.getAxesPermutation([s],l),c=o;u!=null&&(c=$t({inputs:{x:o},backend:t,attrs:{perm:u}}));let p=C.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${o.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=jt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new yx(c.shape,!1,i),g=h.getCustomSetupFunc(d),y=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(y)}if(a){let d=new yx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=C.getUndoAxesPermutation(u),h=$t({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var V$={kernelName:To,backendName:"webgl",kernelFunc:a7};function l7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,weights:s}=e,{size:a,binaryOutput:i}=n;if(o.shape.length===1){let l=t.readSync(o.dataId),u=t.readSync(s.dataId),c=ix(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(o.shape.length===2){let l=t.bufferSync(o),u=t.bufferSync(s),c=q2(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${o.shape.length}.`)}var G$={kernelName:vc,backendName:"webgl",kernelFunc:l7};var Ov=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2447,18 +2447,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function O8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;y.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new kv(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var N$={kernelName:Si,backendName:"webgl",kernelFunc:O8};var Rf=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,x="",b="";o&&(n?x=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function u7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockSize:s,dataFormat:a}=n;x.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=o.shape[0],l=a==="NHWC"?o.shape[1]:o.shape[2],u=a==="NHWC"?o.shape[2]:o.shape[3],c=a==="NHWC"?o.shape[3]:o.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new Ov(d,s,a);return t.runWebGLProgram(h,[o],o.dtype)}var W$={kernelName:$i,backendName:"webgl",kernelFunc:u7};var Mf=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,y="",b="";n&&(o?y=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
-          ${o}
-        }`:s?x=`float activation(float a) {
+          ${n}
+        }`:s?y=`float activation(float a) {
           float b = getLeakyreluAlphaAtOutCoords();
-          ${o}
-        }`:x=`
+          ${n}
+        }`:y=`
           float activation(float x) {
-            ${o}
+            ${n}
           }
-        `,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
-      ${x}
+        `,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${y}
 
       const ivec2 strides = ivec2(${c}, ${p});
       const ivec2 pads = ivec2(${l}, ${u});
@@ -2503,138 +2503,156 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         ${b}
         setOutput(result);
       }
-    `}};var Ff=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,x="int xR; int xC; int xCOffset;";for(let k=0;k<d;k++)for(let D=0;D<h;D++)x+=`
-          vec4 xTexelR${k}C${D*2} = vec4(0.);
-          vec4 wR${k}C${D} = vec4(0.);
-          vec4 xR${k}C${D} = vec4(0.);`;for(let k=0;k<d;k++)for(let D=0;D<g;D++){let T=D*2;if(x+=`
-          xR = xRCorner + ${k*m};
-          xC = xCCorner + ${T*f};
-        `,p===1){if(T<h&&(u%2==1?x+=`
+    `}};var Lf=class{constructor(e,t=!1,n=null,o=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.outChannels/e.inChannels,i=e.inHeight,l=e.inWidth,u=e.padInfo.top,c=e.padInfo.left,p=e.strideHeight,m=e.strideWidth,f=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,g=e.filterWidth,y=g,b=`
+      int xR; int xC; int xCOffset;
+      vec4 wTexel; vec4 previous; vec4 final;`;for(let E=0;E<g;E++)b+=`
+          vec4 xTexelC${E*2};
+          vec4 xC${E};`;for(let E=0;E<h;E++){for(let $=0;$<g;$++)b+=`
+          xTexelC${$*2} = vec4(0.0);
+          xC${$} = vec4(0.0);`;b+=`
+        xR = xRCorner + ${E*f};
+        if (xR >=0 && xR < ${i}) {
+      `;for(let $=0;$<y/2+1;$++){let D=$*2;if(b+=`
+          xC = xCCorner + ${D*d};
+          `,m===1){if(D<g&&(c%2==1?(b+=`
                 xCOffset = xC + 1;
-                if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
+                if (xCOffset >= 0 && xCOffset < ${l}) {
+                  xTexelC${D} = getX(batch, xR, xCOffset, d1);
 
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${i}) {
-                    xTexelR${k}C${T}.zw = vec2(0.);
+                  if (xCOffset + 1 >= ${l}) {
+                    xTexelC${D}.zw = vec2(0.0);
                   }
-                } else {
-                  xTexelR${k}C${T} = vec4(0.);
                 }
+              `,d===1&&D>0?b+=`
+                xC${D} = vec4(xTexelC${D-2}.zw, xTexelC${D}.xy);
+                `:b+=`
+                  xCOffset = xC + 1 - 2;
 
-                xCOffset = xC + 1 - 2;
-                if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
-                  vec4 previous = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset >= 0 && xCOffset < ${l}) {
+                    previous = getX(batch, xR, xCOffset, d1);
 
-                  // Need to manually clear unused channels in case
-                  // we're reading from recycled texture.
-                  if(xCOffset + 1 >= ${i}) {
-                    previous.zw = vec2(0.);
-                  }
-
-                  xR${k}C${T} = vec4(previous.zw, xTexelR${k}C${T}.xy);
-                } else {
-                  xR${k}C${T} = vec4(0, 0, xTexelR${k}C${T}.xy);
-                }
-              `:x+=`
-                if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
-                  xTexelR${k}C${T} = getX(batch, xR, xC, d1);
-                } else {
-                  xTexelR${k}C${T} = vec4(0.);
-                }
-
-                xR${k}C${T} = xTexelR${k}C${T};
-              `,T+1<h)){let R=u%2==0?y.nearestLargerEven(f):f;f%2==0&&u%2==1||f%2!=0&&u%2!=1?(x+=`
-                  xCOffset = xC + ${u%2} + ${R};
-
-                  if(xR >= 0 && xR < ${a} &&
-                    xCOffset >= 0 && xCOffset < ${i}) {
-                    xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
-                  }
-                `,f>1&&(x+=`
-                    xCOffset -= 2;
-                    if(xR >= 0 && xR < ${a} &&
-                      xCOffset >= 0 && xCOffset < ${i}) {
-                      xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
-                    } else {
-                      xTexelR${k}C${T} = vec4(0.);
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= ${l}) {
+                      previous.zw = vec2(0.0);
                     }
-                  `),x+=`
-                  xR${k}C${T+1} = vec4(
-                    xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.xy);
-                `):x+=`
-                  xCOffset = xC + ${R};
 
-                  if(xR >= 0 && xR < ${a} &&
-                    xCOffset >= 0 && xCOffset < ${i}) {
-                    xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
+                    xC${D} = vec4(previous.zw, xTexelC${D}.xy);
+                  } else {
+                    xC${D} = vec4(0.0, 0.0, xTexelC${D}.xy);
+                  }
+                  `):b+=`
+                if (xC >= 0 && xC < ${l}) {
+                  xTexelC${D} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= ${l}) {
+                    xTexelC${D}.zw = vec2(0.0);
                   }
-
-                  xR${k}C${T+1} = xTexelR${k}C${T+2};
-                `}}else T<h&&(x+=`
-              if(xR >= 0 && xR < ${a}) {
-            `,u%2==1?(x+=`
-                xCOffset = xC + 1 - ${p};
-                if(xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${k}C${T} = getX(batch, xR, xCOffset, d1);
-                } else {
-                  xTexelR${k}C${T} = vec4(0.);
                 }
 
-                if(xC + 1 >= 0 && xC + 1 < ${i}) {
-                  xTexelR${k}C${T+2} = getX(batch, xR, xC + 1, d1);
-                } else {
-                  xTexelR${k}C${T+2} = vec4(0.);
+                xC${D} = xTexelC${D};
+                `,D+1<g)){let O=c%2==0?x.nearestLargerEven(d):d;d%2==0&&c%2==1||d%2!=0&&c%2!=1?(b+=`
+                  xCOffset = xC + ${c%2} + ${O};
+
+                  if (xCOffset >= 0 && xCOffset < ${l}) {
+                    xTexelC${D+2} = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= ${l}) {
+                      xTexelC${D+2}.zw = vec2(0.0);
+                    }
+                  }
+                  `,d>1&&(b+=`
+                    xCOffset -= 2;
+                    if (xCOffset >= 0 && xCOffset < ${l}) {
+                      xTexelC${D} = getX(batch, xR, xCOffset, d1);
+                    }
+                    `),b+=`
+                  xC${D+1} = vec4(xTexelC${D}.zw, xTexelC${D+2}.xy);
+                  `):O===1?b+=`
+                    xC${D+1} = xTexelC${D};
+                    `:b+=`
+                    xCOffset = xC + ${O};
+
+                    if (xCOffset >= 0 && xCOffset < ${l}) {
+                      xTexelC${D+2} = getX(batch, xR, xCOffset, d1);
+                      if (xCOffset + 1 >= ${l}) {
+                        xTexelC${D+2}.zw = vec2(0.0);
+                      }
+                    }
+
+                    xC${D+1} = xTexelC${D+2};
+                    `}}else D<g&&(c%2==1?(b+=`
+                xCOffset = xC + 1 - ${m};
+                if(xCOffset >= 0 && xCOffset < ${l}) {
+                  xTexelC${D} = getX(batch, xR, xCOffset, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= ${l}) {
+                    xTexelC${D}.zw = vec2(0.0);
+                  }
                 }
 
-                xR${k}C${T} = vec4(
-                  xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.zw);
-              `,T+1<h&&(x+=`
-                  vec4 final = vec4(0.);
-                  xCOffset = xC + 1 + ${p};
-                  if(xCOffset >= 0 && xCOffset < ${i}) {
+                if(xC + 1 >= 0 && xC + 1 < ${l}) {
+                  xTexelC${D+2} = getX(batch, xR, xC + 1, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xC + 2 >= ${l}) {
+                    xTexelC${D+2}.zw = vec2(0.0);
+                  }
+                }
+
+                xC${D} = vec4(xTexelC${D}.zw, xTexelC${D+2}.zw);
+              `,D+1<g&&(b+=`
+                  final = vec4(0.0);
+                  xCOffset = xC + 1 + ${m};
+                  if(xCOffset >= 0 && xCOffset < ${l}) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
-                  xR${k}C${T+1} = vec4(xTexelR${k}C${T+2}.xy, final.xy);
-                `)):(x+=`
-                if(xC >= 0 && xC < ${i}) {
-                  xTexelR${k}C${T} = getX(batch, xR, xC, d1);
-                } else {
-                  xTexelR${k}C${T} = vec4(0.);
+                  xC${D+1} = vec4(xTexelC${D+2}.xy, final.xy);
+                `)):(b+=`
+                if(xC >= 0 && xC < ${l}) {
+                  xTexelC${D} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= ${l}) {
+                    xTexelC${D}.zw = vec2(0.0);
+                  }
                 }
 
-                xCOffset = xC + ${p};
-                if(xCOffset >= 0 && xCOffset < ${i}) {
-                  xTexelR${k}C${T+2} = getX(batch, xR, xCOffset, d1);
-                } else {
-                  xTexelR${k}C${T+2} = vec4(0.);
+                xCOffset = xC + ${m};
+                if(xCOffset >= 0 && xCOffset < ${l}) {
+                  xTexelC${D+2} = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset + 1 >= ${l}) {
+                    xTexelC${D+2}.zw = vec2(0.);
+                  }
                 }
 
-                xR${k}C${T} = vec4(
-                  xTexelR${k}C${T}.xy, xTexelR${k}C${T+2}.xy);
-              `,T+1<h&&(x+=`
-                  xR${k}C${T+1} = vec4(
-                    xTexelR${k}C${T}.zw, xTexelR${k}C${T+2}.zw);
-                `)),x+="}");T<h&&(x+=`
-            vec4 wTexelR${k}C${T} = getW(${k}, ${T}, d1, q);
-            wR${k}C${T} = vec4(wTexelR${k}C${T}.xz, wTexelR${k}C${T}.xz);
-          `,T+1<h&&(x+=`
-              vec4 wTexelR${k}C${T+1} = getW(${k}, ${T+1}, d1, q);
-              wR${k}C${T+1} =
-                vec4(wTexelR${k}C${T+1}.xz, wTexelR${k}C${T+1}.xz);`))}for(let k=0;k<d;k++)for(let D=0;D<h;D++)x+=`dotProd += xR${k}C${D} * wR${k}C${D};`;let b="",w="";o&&(n?b=`vec4 activation(vec4 a) {
+                xC${D} = vec4(
+                  xTexelC${D}.xy, xTexelC${D+2}.xy);
+              `,D+1<g&&(b+=`
+                  xC${D+1} = vec4(xTexelC${D}.zw, xTexelC${D+2}.zw);
+                `)));D<g&&(b+=`
+            wTexel = getW(${E}, ${D}, d1, q);
+            dotProd += xC${D} * vec4(wTexel.xz, wTexel.xz);
+          `,D+1<g&&(b+=`
+              wTexel = getW(${E}, ${D+1}, d1, q);
+              dotProd += xC${D+1} * vec4(wTexel.xz, wTexel.xz);
+            `))}b+=`
+        }
+      `}let w="",_="";n&&(o?w=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
-          ${o}
-        }`:s?b=`vec4 activation(vec4 a) {
+          ${n}
+        }`:s?w=`vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
-          ${o}
-        }`:b=`vec4 activation(vec4 x) {
-          ${o}
-        }`,w="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
-      ${b}
+          ${n}
+        }`:w=`vec4 activation(vec4 x) {
+          ${n}
+        }`,_="result = activation(result);");let I=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${w}
 
-      const ivec2 strides = ivec2(${c}, ${p});
-      const ivec2 pads = ivec2(${l}, ${u});
+      const ivec2 strides = ivec2(${p}, ${m});
+      const ivec2 pads = ivec2(${u}, ${c});
 
       void main() {
 
@@ -2642,21 +2660,22 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
-        int d1 = d2;
-        int q = 0;
+        int d1 = d2 / ${a};
+        int q = d2 - d1 * ${a};
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;
 
-        vec4 dotProd = vec4(0.);
+        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
+        vec4 dotProd = vec4(0.000000000000001);
 
-        ${x}
+        ${b}
 
-        vec4 result = dotProd;
+        vec4 result = dotProd - vec4(0.000000000000001);
+        ${I}
         ${_}
-        ${w}
         setOutput(result);
       }
-    `}};function P8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=o,c=l;c==null&&(c=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=N.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return W().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new Ff(p):m=new Rf(p),t.runWebGLProgram(m,[n,s],"float32")}var S$={kernelName:Dn,backendName:"webgl",kernelFunc:P8};var vv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
+    `}};function c7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=n,c=l;c==null&&(c=[1,1]),x.assert(C.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=C.computeConv2DInfo(o.shape,s.shape,a,c,i,u,!0),m;return W().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new Lf(p):m=new Mf(p),t.runWebGLProgram(m,[o,s],"float32")}var j$={kernelName:Ao,backendName:"webgl",kernelFunc:c7};var Pv=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,o=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2670,14 +2689,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         // TO DO: Vec4 over the batch size
         for (int b = 0; b < ${e.batchSize}; b++) {
           for (int yR = 0; yR < ${e.outHeight}; yR++) {
-            int xR = wR + yR * ${t} - ${n};
+            int xR = wR + yR * ${t} - ${o};
 
             if (xR < 0 || xR >= ${e.inHeight}) {
               continue;
             }
 
             for (int yC = 0; yC < ${e.outWidth}; yC++) {
-              int xC = wC + yC * ${o} - ${s};
+              int xC = wC + yC * ${n} - ${s};
 
               if (xC < 0 || xC >= ${e.inWidth}) {
                 continue;
@@ -2691,7 +2710,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},Cv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
+    `}},Mv=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,o=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${a}, ${i});
 
       void main() {
@@ -2705,7 +2724,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         float dotProd = 0.0;
 
         for (int wR = 0; wR < ${t}; wR++) {
-          float dyR = float(dyRCorner + wR) / ${n}.0;
+          float dyR = float(dyRCorner + wR) / ${o}.0;
 
           if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
             continue;
@@ -2714,7 +2733,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
           int wRPerm = ${t} - 1 - wR;
 
-          for (int wC = 0; wC < ${o}; wC++) {
+          for (int wC = 0; wC < ${n}; wC++) {
             float dyC = float(dyCCorner + wC) / ${s}.0;
 
             if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
@@ -2723,7 +2742,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
             }
             int idyC = int(dyC);
 
-            int wCPerm = ${o} - 1 - wC;
+            int wCPerm = ${n} - 1 - wC;
 
             // TO DO: Vec4 over the channelMul
             for (int dm = 0; dm < ${l}; dm++) {
@@ -2736,13 +2755,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function M8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=N.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new vv(p);return t.runWebGLProgram(m,[n,s],"float32")}var T$={kernelName:kc,backendName:"webgl",kernelFunc:M8};function L8(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new Cv(p);return t.runWebGLProgram(m,[n,s],"float32")}var A$={kernelName:vc,backendName:"webgl",kernelFunc:L8};var Iv=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function p7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=n,p=C.computeConv2DInfo(o.shape,c,a,i,l,u,!0),m=new Pv(p);return t.runWebGLProgram(m,[o,s],"float32")}var U$={kernelName:Cc,backendName:"webgl",kernelFunc:p7};function m7(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=n,p=C.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new Mv(p);return t.runWebGLProgram(m,[o,s],"float32")}var H$={kernelName:Ic,backendName:"webgl",kernelFunc:m7};var Lv=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function z8(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Iv(s),l=t.runWebGLProgram(i,[a],a.dtype),u=pe({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var E$={kernelName:Cc,backendName:"webgl",kernelFunc:z8};var Nv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=`
+    `}};function f7(r){let{inputs:e,backend:t}=r,{x:n}=e,o=[...n.shape,...n.shape],s=x.sizeFromShape(n.shape),a=ue({inputs:{x:n},backend:t,attrs:{shape:[s]}}),i=new Lv(s),l=t.runWebGLProgram(i,[a],a.dtype),u=ue({inputs:{x:l},backend:t,attrs:{shape:o}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var q$={kernelName:Nc,backendName:"webgl",kernelFunc:f7};var zv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:o,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=o;this.userCode=`
       const ivec2 strides = ivec2(${s}, ${a});
       const ivec2 pads = ivec2(${p}, ${m});
       const float neg_infinity = -3.4e38;
@@ -2764,7 +2783,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
             for (int w = 0; w < ${l}; w++) {
               int wIn = wBeg + w * ${c};
 
-              if (wIn >= 0 && wIn < ${o}) {
+              if (wIn >= 0 && wIn < ${n}) {
                 float xVal = getX(batch, hIn, wIn, d1);
                 float wVal = getW(h, w, d1);
 
@@ -2780,7 +2799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function B8(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new Nv(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=pe({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var D$={kernelName:qa,backendName:"webgl",kernelFunc:B8};var V8="return (x >= 0.0) ? x : (exp(x) - 1.0);",G8=`
+    `}};function d7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s}=e,{strides:a,pad:i,dilations:l}=n,u=C.computeDilation2DInfo(o.shape,s.shape,a,i,"NHWC",l),c,p=new zv(u);c=t.runWebGLProgram(p,[o,s],"float32");let m=ue({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var K$={kernelName:Qa,backendName:"webgl",kernelFunc:d7};function h7(r){let{inputs:e,backend:t,attrs:n}=r,{equation:o}=n,s=e,{allDims:a,summedDims:i,idDims:l}=C.decodeEinsumEquation(o,s.length);C.checkEinsumDimSizes(a.length,l,s);let{path:u,steps:c}=C.getEinsumComputePath(i,l),p=c.length,m=null,f=a.length,d=[];for(let h=0;h<p;++h){for(let g of c[h]){let{permutationIndices:y,expandDims:b}=C.getEinsumPermutation(f,l[g]),w;C.isIdentityPermutation(y)?w=s[g]:(w=$t({inputs:{x:s[g]},backend:t,attrs:{perm:y}}),d.push(w));let _=w.shape.slice();for(let I=0;I<b.length;++I)_.splice(b[I],0,1);x.arraysEqual(w.shape,_)||(w=ue({inputs:{x:w},backend:t,attrs:{shape:_}}),d.push(w)),m===null?m=w:(m=Of({inputs:{a:w,b:m},backend:t}),d.push(m))}h<p-1&&(u[h]>=0&&(m=Pu({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-f),keepDims:!1}}),d.push(m)),f--)}for(let h of d)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var X$={kernelName:Sc,backendName:"webgl",kernelFunc:h7};var g7="return (x >= 0.0) ? x : (exp(x) - 1.0);",x7=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -2789,27 +2808,27 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,W8=_e({opSnippet:V8,packedOpSnippet:G8}),$$={kernelName:Ti,backendName:"webgl",kernelFunc:W8};var j8="return (b >= 1.0) ? a : a * (b + 1.0);",U8=`
+`,y7=_e({opSnippet:g7,packedOpSnippet:x7}),Y$={kernelName:Ri,backendName:"webgl",kernelFunc:y7};var b7="return (b >= 1.0) ? a : a * (b + 1.0);",w7=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,H8=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cs(U8,o.shape,n.shape):new cn(j8,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},R$={kernelName:Ic,backendName:"webgl",kernelFunc:H8};var q8=`
+`,_7=r=>{let{inputs:e,backend:t}=r,{dy:n,y:o}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Is(w7,n.shape,o.shape):new uo(b7,n.shape,o.shape);return t.runWebGLProgram(s,[n,o],n.dtype)},Z$={kernelName:Tc,backendName:"webgl",kernelFunc:_7};var k7=`
   return vec4(equal(a, b));
-`,K8="return float(a == b);",X8=nt({opSnippet:K8,packedOpSnippet:q8,dtype:"bool"}),F$={kernelName:Ei,backendName:"webgl",kernelFunc:X8};var Y8=`
+`,v7="return float(a == b);",C7=st({opSnippet:v7,packedOpSnippet:k7,dtype:"bool"}),J$={kernelName:Oi,backendName:"webgl",kernelFunc:C7};var I7=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
-  float p = ${N.ERF_P};
-  float a1 = ${N.ERF_A1};
-  float a2 = ${N.ERF_A2};
-  float a3 = ${N.ERF_A3};
-  float a4 = ${N.ERF_A4};
-  float a5 = ${N.ERF_A5};
+  float p = ${C.ERF_P};
+  float a1 = ${C.ERF_A1};
+  float a2 = ${C.ERF_A2};
+  float a3 = ${C.ERF_A3};
+  float a4 = ${C.ERF_A4};
+  float a5 = ${C.ERF_A5};
 
   float sign = sign(x);
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,Z8=_e({opSnippet:Y8}),O$={kernelName:Ai,backendName:"webgl",kernelFunc:Z8};var P$="return exp(x);",Sv=_e({opSnippet:P$,packedOpSnippet:P$,cpuKernelImpl:R2}),M$={kernelName:Rn,backendName:"webgl",kernelFunc:Sv};function hx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var L$={kernelName:Ms,backendName:"webgl",kernelFunc:hx};var z$="return exp(x) - 1.0;",J8=_e({opSnippet:z$,packedOpSnippet:z$,cpuKernelImpl:F2}),B$={kernelName:Di,backendName:"webgl",kernelFunc:J8};var gx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,N7=_e({opSnippet:I7}),Q$={kernelName:Fi,backendName:"webgl",kernelFunc:N7};var eR="return exp(x);",Bv=_e({opSnippet:eR,packedOpSnippet:eR,cpuKernelImpl:Y2}),tR={kernelName:Do,backendName:"webgl",kernelFunc:Bv};function bx(r){let{inputs:e,attrs:t,backend:n}=r,{dim:o}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=o;return o<0&&(x.assert(-(a+1)<=o,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+o+1),i.splice(l,0,1),ue({inputs:{x:s},backend:n,attrs:{shape:i}})}var rR={kernelName:Bs,backendName:"webgl",kernelFunc:bx};var nR="return exp(x) - 1.0;",S7=_e({opSnippet:nR,packedOpSnippet:nR,cpuKernelImpl:Z2}),oR={kernelName:Pi,backendName:"webgl",kernelFunc:S7};var wx=class{constructor(e,t,n){this.variableNames=["real","imag"];let o=t[1];this.outputShape=t;let s=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${o}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${s};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -2817,13 +2836,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
       }
 
       float mulMatDFT(int batch, int index) {
-        float indexRatio = float(index) / float(${n});
+        float indexRatio = float(index) / float(${o});
         float exponentMultiplierTimesIndexRatio =
             exponentMultiplier * indexRatio;
 
         float result = 0.0;
 
-        for (int i = 0; i < ${n}; i++) {
+        for (int i = 0; i < ${o}; i++) {
           // x = (-2|2 * PI / N) * index * i;
           float x = exponentMultiplierTimesIndexRatio * float(i);
           float expR = cos(x);
@@ -2842,13 +2861,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function xx(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new gx("real",l,e),c=new gx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=fo({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=pe({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function Q8(r){let{inputs:e,backend:t}=r,{input:o}=e;return xx(o,!1,t)}var V$={kernelName:Nc,backendName:"webgl",kernelFunc:Q8};var Tv=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function _x(r,e,t){let n=t.texData.get(r.dataId),o=x.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=o/s,i=ue({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new wx("real",l,e),c=new wx("imag",l,e),p=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=gn({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=ue({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function T7(r){let{inputs:e,backend:t}=r,{input:n}=e;return _x(n,!1,t)}var sR={kernelName:Ac,backendName:"webgl",kernelFunc:T7};var Vv=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       uniform float value;
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function Of(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s==="string"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new Tv(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var G$={kernelName:Ka,backendName:"webgl",kernelFunc:Of};var Av=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function zf(r){let{backend:e,attrs:t}=r,{shape:n,value:o}=t,{dtype:s}=t;if(s=s||x.inferDtype(o),s==="string"){let a=x.getArrayFromDType(s,x.sizeFromShape(n));return a.fill(o),e.makeTensorInfo(n,s,a)}else{let a=new Vv(n,o),i=a.getCustomSetupFunc(o);return e.runWebGLProgram(a,[],s,i)}}var iR={kernelName:el,backendName:"webgl",kernelFunc:zf};var Gv=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -2862,7 +2881,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
           }
           setOutput(outputValue);
         }
-    `}};var W$={kernelName:$i,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Av(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var j$="return floor(x);",eY=_e({opSnippet:j$,packedOpSnippet:j$,cpuKernelImpl:O2}),U$={kernelName:Fn,backendName:"webgl",kernelFunc:eY};var tY=`
+    `}};var aR={kernelName:Mi,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,n=e,o=new Gv(t.shape);return n.runWebGLProgram(o,[t],t.dtype)}};var lR="return floor(x);",A7=_e({opSnippet:lR,packedOpSnippet:lR,cpuKernelImpl:J2}),uR={kernelName:$o,backendName:"webgl",kernelFunc:A7};var E7=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -2872,7 +2891,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
   } else {
     return NAN;
   }
-`,rY=`
+`,D7=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -2893,13 +2912,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,oY=nt({opSnippet:tY,packedOpSnippet:rY,dtype:"int32"}),H$={kernelName:On,backendName:"webgl",kernelFunc:oY};var Ev=class{constructor(e){this.variableNames=["A"];let t=Rt(),[o,n]=e;this.outputShape=e,this.userCode=`
+`,$7=st({opSnippet:E7,packedOpSnippet:D7,dtype:"int32"}),cR={kernelName:Ro,backendName:"webgl",kernelFunc:$7};var Wv=class{constructor(e){this.variableNames=["A"];let t=Ot(),[n,o]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
         int texC = coords[1];
         int depth = coords[2];
-        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${o}.0, ${n}.0);
 
         vec4 values = ${t.texture2D}(A, uv);
         float value;
@@ -2915,7 +2934,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}};var Dv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Rt(),[o,n]=e;this.outputShape=e,this.userCode=`
+    `}};var jv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ot(),[n,o]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -2930,7 +2949,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
             depth = coords[2] + col;
 
             vec2 uv = (vec2(texC, texR) + halfCR) /
-                       vec2(${n}.0, ${o}.0);
+                       vec2(${o}.0, ${n}.0);
             vec4 values = ${t.texture2D}(A, uv);
             float value;
             if (depth == 0) {
@@ -2949,8 +2968,8 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
         ${t.output} = result;
       }
-    `}};var q$={kernelName:Im,backendName:"webgl",kernelFunc:nY},Np;function nY(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,[l,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,l],p=[u,l,s];(i||a)&&(Np==null&&(Np=document.createElement("canvas").getContext("2d")),Np.canvas.width=l,Np.canvas.height=u,Np.drawImage(n,0,0,l,u),n=Np.canvas);let m=t.makeTensorInfo(c,"int32");t.texData.get(m.dataId).usage=Fr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let f=W().getBool("WEBGL_PACK")?new Dv(p):new Ev(p),d=t.runWebGLProgram(f,[m],"int32");return t.disposeData(m.dataId),d}function sY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),x,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))x=mx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(W().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)x=fx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,k=i!=null,D=f==="leakyrelu",T=f?xl(f,!1):null,R=new $f(g,_,T,k,D),O=[n,s];if(a&&O.push(a),i&&O.push(i),D){let M=t.makeTensorInfo([],"float32",y.createScalarValue(d,"float32"));O.push(M),b.push(M)}x=t.runWebGLProgram(R,O,"float32")}let w=pe({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var K$={kernelName:Xs,backendName:"webgl",kernelFunc:sY};function iY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),y.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=N.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),x=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?xl(m,x):null,w=[n,s],_=a!=null,k=i!=null,D=m==="leakyrelu";if(_&&w.push(a),k&&w.push(i),D){let O=t.makeTensorInfo([],"float32",y.createScalarValue(f,"float32"));w.push(O),d.push(O)}let T;x?T=new Ff(g,_,b,k,D):T=new Rf(g,_,b,k,D);let R=t.runWebGLProgram(T,w,"float32");return d.forEach(O=>t.disposeIntermediateTensorInfo(O)),R}var X$={kernelName:Ys,backendName:"webgl",kernelFunc:iY};var $v=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Le(t.length),s=Le(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
-        ${n} strides = ${n}(${this.strides});
+    `}};var pR={kernelName:Am,backendName:"webgl",kernelFunc:R7},Ap;function R7(r){let{inputs:e,backend:t,attrs:n}=r,{pixels:o}=e,{numChannels:s}=n,a=typeof HTMLVideoElement!="undefined"&&o instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&o instanceof HTMLImageElement,[l,u]=a?[o.videoWidth,o.videoHeight]:[o.width,o.height],c=[u,l],p=[u,l,s];(i||a)&&(Ap==null&&(Ap=document.createElement("canvas").getContext("2d")),Ap.canvas.width=l,Ap.canvas.height=u,Ap.drawImage(o,0,0,l,u),o=Ap.canvas);let m=t.makeTensorInfo(c,"int32");t.texData.get(m.dataId).usage=Rr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),o);let f=W().getBool("WEBGL_PACK")?new jv(p):new Wv(p),d=t.runWebGLProgram(f,[m],"int32");return t.disposeData(m.dataId),d}function F7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=n,h=C.convertConv2DDataFormat(c),g=C.computeConv2DInfo(o.shape,s.shape,l,p,u,m,!1,h),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=gx({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(W().getBool("WEBGL_CONV_IM2COL")&&o.shape[0]===1)y=xx({x:o,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,I=i!=null,E=f==="leakyrelu",$=f?_l(f,!1):null,D=new Pf(g,_,$,I,E),O=[o,s];if(a&&O.push(a),i&&O.push(i),E){let M=t.makeTensorInfo([],"float32",x.createScalarValue(d,"float32"));O.push(M),b.push(M)}y=t.runWebGLProgram(D,O,"float32")}let w=ue({inputs:{x:y},backend:t,attrs:{shape:g.outShape}});return b.push(y),b.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var mR={kernelName:Js,backendName:"webgl",kernelFunc:F7};function O7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=n,d=[],h=c;h==null&&(h=[1,1]),x.assert(C.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=C.computeConv2DInfo(o.shape,s.shape,l,h,u,p,!0),y=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?_l(m,y):null,w=[o,s],_=a!=null,I=i!=null,E=m==="leakyrelu";if(_&&w.push(a),I&&w.push(i),E){let O=t.makeTensorInfo([],"float32",x.createScalarValue(f,"float32"));w.push(O),d.push(O)}let $;y?$=new Lf(g,_,b,I,E):$=new Mf(g,_,b,I,E);let D=t.runWebGLProgram($,w,"float32");return d.forEach(O=>t.disposeIntermediateTensorInfo(O)),D}var fR={kernelName:Qs,backendName:"webgl",kernelFunc:O7};var Uv=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let o=Ve(t.length),s=Ve(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${o} strides = ${o}(${this.strides});
          void main() {
           ${s} coords = getOutputCoords();
           int flattenIndex = 0;
@@ -2960,21 +2979,21 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function aY(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=N.prepareAndValidate(o,n),p=pe({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/u,u]}}),f=new $v(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var Y$={kernelName:Ri,backendName:"webgl",kernelFunc:aY};var Rv=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Le(this.rank),n=lY(e,2);this.userCode=`
+      `}};function P7(r){let{inputs:e,backend:t}=r,{params:n,indices:o}=e,s=o.shape,a=s[s.length-1],[i,l,u,c]=C.prepareAndValidate(n,o),p=ue({inputs:{x:o},backend:t,attrs:{shape:[l,a]}}),m=ue({inputs:{x:n},backend:t,attrs:{shape:[x.sizeFromShape(n.shape)/u,u]}}),f=new Uv(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=ue({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var dR={kernelName:Li,backendName:"webgl",kernelFunc:P7};var Hv=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=Ve(this.rank),o=M7(e,2);this.userCode=`
       void main() {
-        ${o} resRC = getOutputCoords();
-        setOutput(getA(${n}));
+        ${n} resRC = getOutputCoords();
+        setOutput(getA(${o}));
       }
-    `}};function lY(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n<r.length;n++)n===2?o.push("int(getIndices(resRC.x, resRC.z))"):o.push(`${t[n]}`);return o.join()}function uY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,l=y.parseAxisParam(a,n.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=y.sizeFromShape(s.shape),p=[],m=pe({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let b=t.bufferSync(f),w=t.bufferSync(m),_=P2(w,b,d);return p.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,_.dtype,_.values)}let h=new Rv(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var Z$={kernelName:Ls,backendName:"webgl",kernelFunc:uY};var cY="return float(a > b);",pY=`
+    `}};function M7(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let o=0;o<r.length;o++)o===2?n.push("int(getIndices(resRC.x, resRC.z))"):n.push(`${t[o]}`);return n.join()}function L7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o,indices:s}=e,{axis:a,batchDims:i}=n,l=x.parseAxisParam(a,o.shape)[0],u=C.segment_util.collectGatherOpShapeInfo(o,s,l,i),c=x.sizeFromShape(s.shape),p=[],m=ue({inputs:{x:o},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=ue({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([o,s])||o.dtype==="string"){let b=t.bufferSync(f),w=t.bufferSync(m),_=Q2(w,b,d);return p.forEach(I=>t.disposeIntermediateTensorInfo(I)),t.makeTensorInfo(u.outputShape,_.dtype,_.values)}let h=new Hv(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let y=ue({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),y}var hR={kernelName:Vs,backendName:"webgl",kernelFunc:L7};var z7="return float(a > b);",B7=`
   return vec4(greaterThan(a, b));
-`,mY=nt({opSnippet:cY,packedOpSnippet:pY,cpuKernelImpl:M2,dtype:"bool"}),J$={kernelName:Fi,backendName:"webgl",kernelFunc:mY};var fY="return float(a >= b);",dY=`
+`,V7=st({opSnippet:z7,packedOpSnippet:B7,cpuKernelImpl:eD,dtype:"bool"}),gR={kernelName:zi,backendName:"webgl",kernelFunc:V7};var G7="return float(a >= b);",W7=`
   return vec4(greaterThanEqual(a, b));
-`,hY=nt({opSnippet:fY,packedOpSnippet:dY,dtype:"bool"}),Q$={kernelName:Mn,backendName:"webgl",kernelFunc:hY};function gY(r){let{inputs:e,backend:t}=r,{input:o}=e;return xx(o,!0,t)}var eR={kernelName:Sc,backendName:"webgl",kernelFunc:gY};var xY="return float(!isnan(x) && !isinf(x));",yY=_e({opSnippet:xY,dtype:"bool"}),tR={kernelName:Oi,backendName:"webgl",kernelFunc:yY};var bY="return float(isinf(x));",wY=_e({opSnippet:bY,dtype:"bool"}),rR={kernelName:Pi,backendName:"webgl",kernelFunc:wY};var _Y="return float(isnan(x));",kY=_e({opSnippet:_Y,dtype:"bool"}),oR={kernelName:Mi,backendName:"webgl",kernelFunc:kY};var vY="return float(a < b);",CY=`
+`,j7=st({opSnippet:G7,packedOpSnippet:W7,dtype:"bool"}),xR={kernelName:Oo,backendName:"webgl",kernelFunc:j7};function U7(r){let{inputs:e,backend:t}=r,{input:n}=e;return _x(n,!0,t)}var yR={kernelName:Ec,backendName:"webgl",kernelFunc:U7};var H7="return float(!isnan(x) && !isinf(x));",q7=_e({opSnippet:H7,dtype:"bool"}),bR={kernelName:Bi,backendName:"webgl",kernelFunc:q7};var K7="return float(isinf(x));",X7=_e({opSnippet:K7,dtype:"bool"}),wR={kernelName:Vi,backendName:"webgl",kernelFunc:X7};var Y7="return float(isnan(x));",Z7=_e({opSnippet:Y7,dtype:"bool"}),_R={kernelName:Gi,backendName:"webgl",kernelFunc:Z7};var J7="return float(a < b);",Q7=`
   return vec4(lessThan(a, b));
-`,IY=nt({opSnippet:vY,packedOpSnippet:CY,cpuKernelImpl:L2,dtype:"bool"}),nR={kernelName:Li,backendName:"webgl",kernelFunc:IY};var NY="return float(a <= b);",SY=`
+`,eY=st({opSnippet:J7,packedOpSnippet:Q7,cpuKernelImpl:tD,dtype:"bool"}),kR={kernelName:Wi,backendName:"webgl",kernelFunc:eY};var tY="return float(a <= b);",rY=`
   return vec4(lessThanEqual(a, b));
-`,TY=nt({opSnippet:NY,packedOpSnippet:SY,dtype:"bool"}),sR={kernelName:zi,backendName:"webgl",kernelFunc:TY};function AY(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=z2(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var iR={kernelName:Ac,backendName:"webgl",kernelFunc:AY};var EY=`if (x < 0.0) return NAN;
-  return log(x);`,DY=`
+`,nY=st({opSnippet:tY,packedOpSnippet:rY,dtype:"bool"}),vR={kernelName:ji,backendName:"webgl",kernelFunc:nY};function oY(r){let{backend:e,attrs:t}=r,{start:n,stop:o,num:s}=t,a=rD(n,o,s);return e.makeTensorInfo([a.length],"float32",a)}var CR={kernelName:$c,backendName:"webgl",kernelFunc:oY};var sY=`if (x < 0.0) return NAN;
+  return log(x);`,iY=`
   vec4 result = log(x);
   vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
   result.r = isNaN.r == 1.0 ? NAN : result.r;
@@ -2983,16 +3002,16 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
   result.a = isNaN.a == 1.0 ? NAN : result.a;
 
   return result;
-`,$Y=_e({opSnippet:EY,packedOpSnippet:DY,cpuKernelImpl:B2}),aR={kernelName:zn,backendName:"webgl",kernelFunc:$Y};var RY="return log(1.0 + x);",FY=_e({opSnippet:RY}),lR={kernelName:Bi,backendName:"webgl",kernelFunc:FY};var OY="return float(a >= 1.0 && b >= 1.0);",PY=`
+`,aY=_e({opSnippet:sY,packedOpSnippet:iY,cpuKernelImpl:nD}),IR={kernelName:Mo,backendName:"webgl",kernelFunc:aY};var lY="return log(1.0 + x);",uY=_e({opSnippet:lY}),NR={kernelName:Ui,backendName:"webgl",kernelFunc:uY};var cY="return float(a >= 1.0 && b >= 1.0);",pY=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,MY=nt({opSnippet:OY,packedOpSnippet:PY,dtype:"bool"}),uR={kernelName:Vi,backendName:"webgl",kernelFunc:MY};var LY="return float(!(x >= 1.0));",zY=_e({opSnippet:LY}),cR={kernelName:Ll,backendName:"webgl",kernelFunc:zY};var BY="return float(a >= 1.0 || b >= 1.0);",VY=`
+`,mY=st({opSnippet:cY,packedOpSnippet:pY,dtype:"bool"}),SR={kernelName:Hi,backendName:"webgl",kernelFunc:mY};var fY="return float(!(x >= 1.0));",dY=_e({opSnippet:fY}),TR={kernelName:jl,backendName:"webgl",kernelFunc:dY};var hY="return float(a >= 1.0 || b >= 1.0);",gY=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,GY=nt({opSnippet:BY,packedOpSnippet:VY,dtype:"bool"}),pR={kernelName:zl,backendName:"webgl",kernelFunc:GY};var Fv=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
+`,xY=st({opSnippet:hY,packedOpSnippet:gY,dtype:"bool"}),AR={kernelName:Ul,backendName:"webgl",kernelFunc:xY};var qv=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3011,7 +3030,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         float val = x * ${l};
         setOutput(val);
       }
-    `}};var Ov=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
+    `}};var Kv=class{constructor(e,t,n,o,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${n}) + float(${o}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3073,7 +3092,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         vec4 result = xAtOutputCoords * ${l};
         setOutput(result);
       }
-    `}};var WY=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new Ov(n.shape,s,a,i,l):new Fv(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},mR={kernelName:Xa,backendName:"webgl",kernelFunc:WY};var Pv=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`
+    `}};var yY=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=n,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new Kv(o.shape,s,a,i,l):new qv(o.shape,s,a,i,l);return t.runWebGLProgram(u,[o],o.dtype)},ER={kernelName:tl,backendName:"webgl",kernelFunc:yY};var Xv=class{constructor(e,t,n,o,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=o,this.beta=s,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3102,14 +3121,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
             }
           }
 
-          norm = float(${n}) * norm + float(${o});
+          norm = float(${o}) * norm + float(${n});
 
           for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
             if (k < depthBegin){
               continue;
             }
             else if (k >= depthBegin && k < depthEnd){
-              float dyi = -2.0 * float(${n})
+              float dyi = -2.0 * float(${o})
                 * float(${s})
                 * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
                 / norm;
@@ -3128,14 +3147,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
       }
       setOutput(result);
       }
-    `}};var jY=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new Pv(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},fR={kernelName:Ec,backendName:"webgl",kernelFunc:jY};function dR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=pe({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=To(i,r.dtype,"max",o),u=pe({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function Mv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let w=t.texData.get(f.dataId).values,_=new Array(i);for(let T=0;T<_.length;T++)_[T]=n.shape[c[T]];let k=Tu(w,n.shape,n.dtype,c,_);f=t.makeTensorInfo(_,n.dtype);let D=t.texData.get(f.dataId);D.values=k}else f=yl(n,c,t);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[d,h]=N.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=N.expandShapeToKeepDim(d,l));let x;if(m){let w=t.texData.get(f.dataId).values,_=V2(w,y.sizeFromShape(h),g,n.dtype);x=t.makeTensorInfo(g,n.dtype);let k=t.texData.get(x.dataId);k.values=_}else x=dR(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),x}var hR={kernelName:Bn,backendName:"webgl",kernelFunc:Mv};var UY=sx+`
+    `}};var bY=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=n,p=new Xv(o.shape,i,l,u,c);return t.runWebGLProgram(p,[o,s,a],o.dtype)},DR={kernelName:Rc,backendName:"webgl",kernelFunc:bY};function $R(r,e,t,n){let o=x.sizeFromShape(e),a=x.sizeFromShape(r.shape)/o,i=ue({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=An(i,r.dtype,"max",n),u=ue({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}function Yv(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reductionIndices:s,keepDims:a}=n,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=C.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([o]),f=o;if(p){if(m){let w=t.texData.get(f.dataId).values,_=new Array(i);for(let $=0;$<_.length;$++)_[$]=o.shape[c[$]];let I=Fu(w,o.shape,o.dtype,c,_);f=t.makeTensorInfo(_,o.dtype);let E=t.texData.get(f.dataId);E.values=I}else f=kl(o,c,t);u=C.getInnerMostAxes(u.length,i)}C.assertAxesAreInnerMostDims("max",u,i);let[d,h]=C.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=C.expandShapeToKeepDim(d,l));let y;if(m){let w=t.texData.get(f.dataId).values,_=oD(w,x.sizeFromShape(h),g,o.dtype);y=t.makeTensorInfo(g,o.dtype);let I=t.texData.get(y.dataId);I.values=_}else y=$R(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),y}var RR={kernelName:Lo,backendName:"webgl",kernelFunc:Yv};var wY=ux+`
   return max(a, b);
-`,HY=`
+`,_Y=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+gl+`
+  `+wl+`
   return result;
-`,qY=nt({opSnippet:UY,packedOpSnippet:HY,cpuKernelImpl:G2}),gR={kernelName:Vn,backendName:"webgl",kernelFunc:qY};function KY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;_s(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;y.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new ii(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var xR={kernelName:Gn,backendName:"webgl",kernelFunc:KY};function XY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new Du(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var yR={kernelName:Ya,backendName:"webgl",kernelFunc:XY};var Lv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
+`,kY=st({opSnippet:wY,packedOpSnippet:_Y,cpuKernelImpl:sD}),FR={kernelName:zo,backendName:"webgl",kernelFunc:kY};function vY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e;ks(o,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=n,u=1;x.assert(C.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=C.computePool2DInfo(o.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:o},backend:t});let p=new ui(c,"max",!1);return t.runWebGLProgram(p,[o],o.dtype)}var OR={kernelName:Bo,backendName:"webgl",kernelFunc:vY};function CY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=n,c=[1,1,1],p=C.computePool3DInfo(o.shape,s,a,c,i,u,l),m=new Lu(p,"max",!1);return t.runWebGLProgram(m,[o],o.dtype)}var PR={kernelName:rl,backendName:"webgl",kernelFunc:CY};var Zv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,o=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${l});
 
       void main() {
@@ -3151,7 +3170,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         // ? = to be determined. : = across all values in that axis.
         float dotProd = 0.0;
         for (int wR = 0; wR < ${s};
-          wR += ${n}) {
+          wR += ${o}) {
           float dyR = float(dyRCorner + wR) / ${t}.0;
 
           if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
@@ -3160,7 +3179,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
           int idyR = int(dyR);
 
           for (int wC = 0; wC < ${a}; wC++) {
-            float dyC = float(dyCCorner + wC) / ${o}.0;
+            float dyC = float(dyCCorner + wC) / ${n}.0;
 
             if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                 fract(dyC) > 0.0) {
@@ -3181,7 +3200,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},zv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
+    `}},Jv=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,o=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
       const ivec3 pads = ivec3(${p}, ${m}, ${f});
 
       void main() {
@@ -3210,7 +3229,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
           for (int wR = 0; wR < ${u};
               wR += ${a}) {
-            float dyR = float(dyRCorner + wR) / ${o}.0;
+            float dyR = float(dyRCorner + wR) / ${n}.0;
 
             if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
                 fract(dyR) > 0.0) {
@@ -3220,7 +3239,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
             for (int wC = 0; wC < ${c};
                 wC += ${i}) {
-              float dyC = float(dyCCorner + wC) / ${n}.0;
+              float dyC = float(dyCCorner + wC) / ${o}.0;
 
               if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
                   fract(dyC) > 0.0) {
@@ -3245,14 +3264,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function YY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new Du(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new zv(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var bR={kernelName:$c,backendName:"webgl",kernelFunc:YY};function ZY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;_s([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new ii(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new Lv(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var wR={kernelName:Dc,backendName:"webgl",kernelFunc:ZY};function _R(r,e,t,o){let n=new ii(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new ii(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var kR={kernelName:Rc,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,n,s,u,a),[p,m]=_R(o,i,c,l);return[p,m]}};function vR(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=pe({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=To(i,"float32","mean",o),u=pe({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var CR={kernelName:Wn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=y.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let _=a.texData.get(d.dataId).values,k=new Array(i);for(let R=0;R<k.length;R++)k[R]=o.shape[c[R]];let D=Tu(_,o.shape,o.dtype,c,k);d=a.makeTensorInfo(k,o.dtype);let T=a.texData.get(d.dataId);T.values=D}else d=yl(o,c,a);f.push(d),u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=N.computeOutAndReduceShapes(d.shape,u),x=h;n&&(x=N.expandShapeToKeepDim(h,l));let b=vR(d,g,x,a);for(let w of f)a.disposeIntermediateTensorInfo(w);return b}};function JY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=y.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,n.shape.length)),N.assertAxesAreInnerMostDims("min",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=y.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"min",t),x;if(a){let b=N.expandShapeToKeepDim(m,l);x=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),x}var IR={kernelName:jn,backendName:"webgl",kernelFunc:JY};var QY=sx+`
+    `}};function IY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=n,p=[1,1,1],m=C.computePool3DInfo(a.shape,i,l,p,u,c),f=new Lu(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new Jv(m),g=t.runWebGLProgram(h,[o,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var MR={kernelName:Oc,backendName:"webgl",kernelFunc:IY};function NY(r){let{inputs:e,backend:t,attrs:n}=r,{dy:o,input:s,output:a}=e,i=s;ks([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=n,m=C.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new ui(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new Zv(m),y=t.runWebGLProgram(g,[o,h],i.dtype);return t.disposeIntermediateTensorInfo(h),y}var LR={kernelName:Fc,backendName:"webgl",kernelFunc:NY};function zR(r,e,t,n){let o=new ui(t,"max",!1),s=n.runWebGLProgram(o,[r],"float32");o=new ui(t,"max",!0,!0,e);let a=n.runWebGLProgram(o,[r],"float32");return[s,a]}var BR={kernelName:Pc,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{filterSize:o,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;x.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];x.assert(C.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=C.computePool2DInfo(n.shape,o,s,u,a),[p,m]=zR(n,i,c,l);return[p,m]}};function VR(r,e,t,n){let o=x.sizeFromShape(e),a=x.sizeFromShape(r.shape)/o,i=ue({inputs:{x:r},attrs:{shape:[a,o]},backend:n}),l=An(i,"float32","mean",n),u=ue({inputs:{x:l},attrs:{shape:t},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}var GR={kernelName:Vo,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:n}=r,{keepDims:o,axis:s}=e,a=t,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=C.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([n]),f=[],d=n;if(p){if(m){let _=a.texData.get(d.dataId).values,I=new Array(i);for(let D=0;D<I.length;D++)I[D]=n.shape[c[D]];let E=Fu(_,n.shape,n.dtype,c,I);d=a.makeTensorInfo(I,n.dtype);let $=a.texData.get(d.dataId);$.values=E}else d=kl(n,c,a);f.push(d),u=C.getInnerMostAxes(u.length,i)}C.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=C.computeOutAndReduceShapes(d.shape,u),y=h;o&&(y=C.expandShapeToKeepDim(h,l));let b=VR(d,g,y,a);for(let w of f)a.disposeIntermediateTensorInfo(w);return b}};function SY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=C.getAxesPermutation(u,i),p=o;c!=null&&(p=$t({inputs:{x:o},backend:t,attrs:{perm:c}}),u=C.getInnerMostAxes(u.length,o.shape.length)),C.assertAxesAreInnerMostDims("min",u,i);let[m,f]=C.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=ue({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=An(h,h.dtype,"min",t),y;if(a){let b=C.expandShapeToKeepDim(m,l);y=ue({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=ue({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var WR={kernelName:Go,backendName:"webgl",kernelFunc:SY};var TY=ux+`
   return min(a, b);
-`,e7=`
+`,AY=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+gl+`
+  `+wl+`
   return result;
-`,t7=nt({opSnippet:QY,packedOpSnippet:e7,cpuKernelImpl:W2}),NR={kernelName:Un,backendName:"webgl",kernelFunc:t7};var Bv=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let n=e.length,s=Le(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=`
+`,EY=st({opSnippet:TY,packedOpSnippet:AY,cpuKernelImpl:iD}),jR={kernelName:Wo,backendName:"webgl",kernelFunc:EY};var Qv=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let o=e.length,s=Ve(o),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o),u=n==="reflect"?0:1;if(o===1){this.userCode=`
         int start = ${a};
         int end = ${i};
 
@@ -3271,7 +3290,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
 
       void main() {
         ${s} outC = getOutputCoords();
-        for (int i = 0; i < ${n}; i++) {
+        for (int i = 0; i < ${o}; i++) {
           if (outC[i] < start[i]) {
             outC[i] = start[i] * 2 - outC[i] - ${u};
           } else if(outC[i] >= end[i]) {
@@ -3281,7 +3300,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         ${s} coords = outC - start;
         setOutput(getX(${l}));
       }
-    `}};var Vv=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Le(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Wt("rc",n),u=Wt("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===1){let d=`
+    `}};var eC=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let o=e.length,s=Ve(o),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Wt("rc",o),u=Wt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=n==="reflect"?0:1,f="";if(o===1){let d=`
         ${s} source = rc;
         if (source < start) {
           source = start * 2 - source - ${m};
@@ -3293,7 +3312,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         ${s} rc = outputLoc;
         ${d}
         result[0] = getChannel(getX(${u.join()}), ${p});
-        ${l[n-1]} += 1;
+        ${l[o-1]} += 1;
         if(${c}) {
           ${d}
           result[1] = getChannel(getX(${u.join()}), ${p});
@@ -3311,17 +3330,17 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         ${s} rc = outputLoc;
         ${d}
         result[0] = getChannel(getX(${u.join()}), ${p});
-        ${l[n-1]} += 1;
+        ${l[o-1]} += 1;
         if(${c}) {
           ${d}
           result[1] = getChannel(getX(${u.join()}), ${p});
         }
         rc = outputLoc;
-        ${l[n-2]} += 1;
-        if(${l[n-2]} < ${this.outputShape[n-2]}) {
+        ${l[o-2]} += 1;
+        if(${l[o-2]} < ${this.outputShape[o-2]}) {
           ${d}
           result[2] = getChannel(getX(${u.join()}), ${p});
-          ${l[n-1]} += 1;
+          ${l[o-1]} += 1;
           if(${c}) {
             ${d}
             result[3] = getChannel(getX(${u.join()}), ${p});
@@ -3337,13 +3356,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         ${f}
         setOutput(result);
       }
-    `}};var r7=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Vv(o.shape,n,s):new Bv(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},SR={kernelName:Za,backendName:"webgl",kernelFunc:r7};var o7=`if (b == 0.0) return NAN;
-  return mod(a, b);`,n7=`
+    `}};var DY=({inputs:r,backend:e,attrs:t})=>{let{x:n}=r,{paddings:o,mode:s}=t,a=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eC(n.shape,o,s):new Qv(n.shape,o,s);return e.runWebGLProgram(a,[n],n.dtype)},UR={kernelName:jo,backendName:"webgl",kernelFunc:DY};var $Y=`if (b == 0.0) return NAN;
+  return mod(a, b);`,RY=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+gl+`
+  `+wl+`
   return result;
-`,s7=nt({opSnippet:o7,packedOpSnippet:n7}),TR={kernelName:Gi,backendName:"webgl",kernelFunc:s7};var Gv=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],this.userCode=`
+`,FY=st({opSnippet:$Y,packedOpSnippet:RY}),HR={kernelName:qi,backendName:"webgl",kernelFunc:FY};var tC=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`
       uniform float seed;
 
       void main() {
@@ -3365,11 +3384,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,i8=_e({opSnippet:s8}),UD={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}getCustomSetupFunc(e){return(t,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var i7=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var OY=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,a7=`
+return a / b;`,PY=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3387,14 +3406,14 @@ return a / b;`,a7=`
   }
 
   return result;
-`,Wv=nt({opSnippet:i7,packedOpSnippet:a7,checkOutOfBounds:!0}),AR={kernelName:$n,backendName:"webgl",kernelFunc:Wv};var ER="return a - b;",jv=nt({opSnippet:ER,packedOpSnippet:ER,supportsComplex:!0,cpuKernelImpl:Z2}),DR={kernelName:us,backendName:"webgl",kernelFunc:jv};function Uv(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=y.parseAxisParam([s],n.shape),i=Mv({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=pe({inputs:{x:i},backend:t,attrs:{shape:l}}),c=jv({inputs:{a:n,b:u},backend:t}),p=Sv({inputs:{x:c},backend:t}),m=Df({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=pe({inputs:{x:m},backend:t,attrs:{shape:l}}),d=Wv({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var $R={kernelName:as,backendName:"webgl",kernelFunc:Uv};function l7(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:Uv({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new Gv(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var RR={kernelName:Fc,backendName:"webgl",kernelFunc:l7};var FR="return -x;";function u7(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=U2(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new vs(o.shape,FR):n=new mo(o.shape,FR),t.runWebGLProgram(n,[o],o.dtype)}var OR={kernelName:zs,backendName:"webgl",kernelFunc:u7};var c7=$r.nonMaxSuppressionV3Impl;function p7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=c7(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var PR={kernelName:ji,backendName:"webgl",kernelFunc:p7};var m7=$r.nonMaxSuppressionV4Impl;function f7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=m7(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var MR={kernelName:Ui,backendName:"webgl",kernelFunc:f7};var d7=$r.nonMaxSuppressionV5Impl;function h7(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:x}=d7(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([x.length],"float32",new Float32Array(x))]}var LR={kernelName:Hi,backendName:"webgl",kernelFunc:h7};var Hv=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`,rC=st({opSnippet:OY,packedOpSnippet:PY,checkOutOfBounds:!0}),qR={kernelName:Eo,backendName:"webgl",kernelFunc:rC};var KR="return a - b;",nC=st({opSnippet:KR,packedOpSnippet:KR,supportsComplex:!0,cpuKernelImpl:dD}),XR={kernelName:ls,backendName:"webgl",kernelFunc:nC};function oC(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{dim:s}=n,a=x.parseAxisParam([s],o.shape),i=Yv({inputs:{x:o},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=C.expandShapeToKeepDim(i.shape,a),u=ue({inputs:{x:i},backend:t,attrs:{shape:l}}),c=nC({inputs:{a:o,b:u},backend:t}),p=Bv({inputs:{x:c},backend:t}),m=Pu({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=ue({inputs:{x:m},backend:t,attrs:{shape:l}}),d=rC({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var YR={kernelName:is,backendName:"webgl",kernelFunc:oC};function MY(r){let{inputs:e,backend:t,attrs:n}=r,{logits:o}=e,{numSamples:s,seed:a,normalized:i}=n,l=i?o:oC({inputs:{logits:o},backend:t,attrs:{dim:o.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new tC(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var ZR={kernelName:Mc,backendName:"webgl",kernelFunc:MY};var JR="return -x;";function LY(r){let{inputs:e,backend:t}=r,{x:n}=e;if(t.shouldExecuteOnCPU([n])){let s=t.texData.get(n.dataId),[a,i]=lD(s.values,n.shape,n.dtype);return t.makeTensorInfo(i,n.dtype,a)}let o;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?o=new Cs(n.shape,JR):o=new hn(n.shape,JR),t.runWebGLProgram(o,[n],n.dtype)}var QR={kernelName:Gs,backendName:"webgl",kernelFunc:LY};var zY=Dr.nonMaxSuppressionV3Impl;function BY(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=n,u=t.readSync(o.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=zY(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var eF={kernelName:Xi,backendName:"webgl",kernelFunc:BY};var VY=Dr.nonMaxSuppressionV4Impl;function GY(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=VY(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var tF={kernelName:Yi,backendName:"webgl",kernelFunc:GY};var WY=Dr.nonMaxSuppressionV5Impl;function jY(r){C.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:n}=r,{boxes:o,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=n,c=t.readSync(o.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:y}=WY(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var rF={kernelName:Zi,backendName:"webgl",kernelFunc:jY};var sC=class{constructor(e,t,n,o){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
-        setOutput(mix(float(${n}), float(${o}),
+        setOutput(mix(float(${o}), float(${n}),
                       float(index == coords.y)));
       }
-    `}};var g7=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=y.sizeFromShape(n.shape),u=new Hv(l,s,a,i),c=pe({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=pe({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},zR={kernelName:qn,backendName:"webgl",kernelFunc:g7};function Pf(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Sa({inputs:{input:o},backend:t}),s=Pf({inputs:{x:n},backend:t}),a=$u({inputs:{input:o},backend:t}),i=Pf({inputs:{x:a},backend:t}),l=fo({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Of({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var BR={kernelName:qs,backendName:"webgl",kernelFunc:Pf};function VR(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Sa({inputs:{input:o},backend:t}),s=VR({inputs:{x:n},backend:t}),a=$u({inputs:{input:o},backend:t}),i=Pf({inputs:{x:a},backend:t}),l=fo({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return Of({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var GR={kernelName:Bs,backendName:"webgl",kernelFunc:VR};function x7(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return hx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),y.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=hx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=dv({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var WR={kernelName:Vs,backendName:"webgl",kernelFunc:x7};var qv=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Le(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
+    `}};var UY=r=>{let{inputs:e,backend:t,attrs:n}=r,{indices:o}=e,{depth:s,onValue:a,offValue:i}=n,l=x.sizeFromShape(o.shape),u=new sC(l,s,a,i),c=ue({inputs:{x:o},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],o.dtype);t.disposeIntermediateTensorInfo(c);let m=[...o.shape,s],f=ue({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},nF={kernelName:Ho,backendName:"webgl",kernelFunc:UY};function Bf(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="complex64"){let o=Ra({inputs:{input:n},backend:t}),s=Bf({inputs:{x:o},backend:t}),a=zu({inputs:{input:n},backend:t}),i=Bf({inputs:{x:a},backend:t}),l=gn({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return zf({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:t})}var oF={kernelName:Ys,backendName:"webgl",kernelFunc:Bf};function sF(r){let{inputs:e,backend:t}=r,{x:n}=e;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let o=Ra({inputs:{input:n},backend:t}),s=sF({inputs:{x:o},backend:t}),a=zu({inputs:{input:n},backend:t}),i=Bf({inputs:{x:a},backend:t}),l=gn({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return zf({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:t})}var iF={kernelName:Ws,backendName:"webgl",kernelFunc:sF};function HY(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n;if(e.length===1)return bx({inputs:{input:e[0]},backend:t,attrs:{dim:o}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{x.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),x.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=bx({inputs:{input:c},backend:t,attrs:{dim:o}});return i.push(p),p}),u=Sv({inputs:l,backend:t,attrs:{axis:o}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var aF={kernelName:js,backendName:"webgl",kernelFunc:HY};var iC=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let o=e.length,s=Ve(o),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,o);if(o===1){this.userCode=`
         int start = ${a};
         int end = ${i};
         uniform float value;
@@ -3421,13 +3440,13 @@ return a / b;`,a7=`
           setOutput(getX(${l}));
         }
       }
-    `}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var Kv=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Le(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Wt("rc",n),u=Wt("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1;
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var aC=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let o=e.length,s=Ve(o),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Wt("rc",o),u=Wt("source",o),c=`${l[o-1]} < ${this.outputShape[o-1]}`,p=o===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[o-1]} += 1;
        if(${c}) {
-      `,n===1?"":`}
+      `,o===1?"":`}
        rc = outputLoc;
-       ${l[n-2]} += 1;
-       if(${l[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":`  ${l[n-1]} += 1;
-         if(${c}) {`],f=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=n===1?2:4;h<g;h++)d+=`
+       ${l[o-2]} += 1;
+       if(${l[o-2]} < ${this.outputShape[o-2]}) {`,o===1?"":`  ${l[o-1]} += 1;
+         if(${c}) {`],f=o===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=o===1?2:4;h<g;h++)d+=`
         ${m[h]}
         if (${f}) {
           result[${h}] = float(value);
@@ -3435,7 +3454,7 @@ return a / b;`,a7=`
           ${s} source = rc - start;
           result[${h}] = getChannel(getX(${u.join()}), ${p});
         }
-      `;d+=n===1?"} ":"}}",this.userCode=`
+      `;d+=o===1?"} ":"}}",this.userCode=`
       const ${s} start = ${s}(${a});
       const ${s} end = ${s}(${i});
       uniform float value;
@@ -3446,7 +3465,7 @@ return a / b;`,a7=`
         ${d}
         setOutput(result);
       }
-    `}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var Xv=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kv(n.shape,s,a):new qv(n.shape,s,a),l=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[n],n.dtype,l)},jR={kernelName:Kn,backendName:"webgl",kernelFunc:Xv};var y7=`
+    `}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};var lC=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{paddings:s,constantValue:a}=n,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aC(o.shape,s,a):new iC(o.shape,s,a),l=i.getCustomSetupFunc(a);return t.runWebGLProgram(i,[o],o.dtype,l)},lF={kernelName:qo,backendName:"webgl",kernelFunc:lC};var qY=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3455,7 +3474,7 @@ return a / b;`,a7=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,b7=`
+`,KY=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3469,11 +3488,11 @@ return a / b;`,a7=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+gl+`
+  `+wl+`
   return result;
-`,w7=nt({opSnippet:y7,packedOpSnippet:b7}),UR={kernelName:Xn,backendName:"webgl",kernelFunc:w7};function _7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=y.parseAxisParam(s,n.shape),c=u,p=N.getAxesPermutation(c,i),m=n;p!=null&&(m=Mt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:x}=H2(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,x,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=y.sizeFromShape(h),x=pe({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=Gl(n.dtype),w=To(x,b,"prod",t);f=pe({inputs:{x:w},backend:t,attrs:{shape:d}}),l.push(x),l.push(w)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=pe({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var HR={kernelName:qi,backendName:"webgl",kernelFunc:_7};var Yv=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=q2(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},qR={kernelName:Ja,backendName:"webgl",kernelFunc:Yv};var k7="return 1.0 / x;",v7=_e({opSnippet:k7}),KR={kernelName:Ki,backendName:"webgl",kernelFunc:v7};var C7=xr+`
+`,XY=st({opSnippet:qY,packedOpSnippet:KY}),uF={kernelName:Ko,backendName:"webgl",kernelFunc:XY};function YY(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{axis:s,keepDims:a}=n,i=o.shape.length,l=[],u=x.parseAxisParam(s,o.shape),c=u,p=C.getAxesPermutation(c,i),m=o;p!=null&&(m=$t({inputs:{x:o},backend:t,attrs:{perm:p}}),c=C.getInnerMostAxes(c.length,i),l.push(m)),C.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:y}=uD(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,y,h)}else{let[d,h]=C.computeOutAndReduceShapes(m.shape,c),g=x.sizeFromShape(h),y=ue({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=Kl(o.dtype),w=An(y,b,"prod",t);f=ue({inputs:{x:w},backend:t,attrs:{shape:d}}),l.push(y),l.push(w)}if(a){l.push(f);let d=C.expandShapeToKeepDim(f.shape,u);f=ue({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var cF={kernelName:Ji,backendName:"webgl",kernelFunc:YY};var uC=r=>{let{backend:e,attrs:t}=r,{start:n,stop:o,step:s,dtype:a}=t,i=cD(n,o,s,a);return e.makeTensorInfo([i.length],a,i)},pF={kernelName:nl,backendName:"webgl",kernelFunc:uC};var ZY="return 1.0 / x;",JY=_e({opSnippet:ZY}),mF={kernelName:Qi,backendName:"webgl",kernelFunc:JY};var QY=yr+`
   return (x < 0.0) ? 0.0 : x;
-`,I7=`
+`,e9=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3483,9 +3502,9 @@ return a / b;`,a7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,N7=_e({opSnippet:C7,packedOpSnippet:I7}),XR={kernelName:Zn,backendName:"webgl",kernelFunc:N7};var S7=xr+`
+`,t9=_e({opSnippet:QY,packedOpSnippet:e9}),fF={kernelName:Yo,backendName:"webgl",kernelFunc:t9};var r9=yr+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,T7=`
+`,n9=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3495,7 +3514,7 @@ return a / b;`,a7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,A7=_e({opSnippet:S7,packedOpSnippet:T7}),YR={kernelName:Qn,backendName:"webgl",kernelFunc:A7};var Zv=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,o9=_e({opSnippet:r9,packedOpSnippet:n9}),dF={kernelName:Jo,backendName:"webgl",kernelFunc:o9};var cC=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/p[0]},
           ${c[1]/p[1]});
@@ -3528,7 +3547,7 @@ return a / b;`,a7=`
 
         setOutput(newValue);
       }
-    `}};var Jv=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};var pC=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${c[0]/p[0]},
           ${c[1]/p[1]},
@@ -3557,7 +3576,7 @@ return a / b;`,a7=`
 
         // Should we calculate next column and row elements in 2x2 packed cell.
         bool hasNextCol = d < ${u-1};
-        bool hasNextRow = coords.z < ${o-1};
+        bool hasNextRow = coords.z < ${n-1};
 
         // In parallel, construct four corners for all four components in
         // packed 2x2 cell.
@@ -3605,7 +3624,7 @@ return a / b;`,a7=`
 
         setOutput(newValue);
       }
-    `}};function E7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Jv(n.shape,l,u,s,a):new Zv(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var ZR={kernelName:Jn,backendName:"webgl",kernelFunc:E7};var Qv=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
+    `}};function s9(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new pC(o.shape,l,u,s,a):new cC(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],"float32")}var hF={kernelName:Zo,backendName:"webgl",kernelFunc:s9};var mC=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3650,7 +3669,7 @@ return a / b;`,a7=`
 
             float dxR = float(dyR) * heightScale;
             int topDxRIndex = int(floor(dxR));
-            int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));
+            int bottomDxRIndex = int(min(ceil(dxR), ${o-1}.0));
             float dxRLerp = dxR - float(topDxRIndex);
             float inverseDxRLerp = 1.0 - dxRLerp;
 
@@ -3686,7 +3705,7 @@ return a / b;`,a7=`
 
         setOutput(accumulator);
       }
-    `}};function D7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new Qv(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var JR={kernelName:Mc,backendName:"webgl",kernelFunc:D7};var eC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function i9(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new mC(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var gF={kernelName:Bc,backendName:"webgl",kernelFunc:i9};var fC=class{constructor(e,t,n,o,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,n,u];let c=[o&&t>1?i-1:i,o&&n>1?l-1:l],p=[o&&t>1?t-1:t,o&&n>1?n-1:n],m=o?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${c[0]/p[0]},
           ${c[1]/p[1]});
@@ -3708,7 +3727,7 @@ return a / b;`,a7=`
 
         setOutput(newValue);
       }
-    `}};function $7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new eC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var QR={kernelName:Qa,backendName:"webgl",kernelFunc:$7};var tC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
+    `}};function a9(r){let{inputs:e,backend:t,attrs:n}=r,{images:o}=e,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,c=new fC(o.shape,l,u,s,a);return t.runWebGLProgram(c,[o],o.dtype)}var xF={kernelName:ol,backendName:"webgl",kernelFunc:a9};var dC=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,o,s]=t,[,a,i]=e,l=[n&&a>1?o-1:o,n&&i>1?s-1:s],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3760,13 +3779,13 @@ return a / b;`,a7=`
                   (float(dyC) / float(${u[1]}));
 
             int sourceNearestRow = int(min(
-                float(int(${n}) - 1),
-                ${o} ? float(round(sourceFracRow)) :
+                float(int(${o}) - 1),
+                ${n} ? float(round(sourceFracRow)) :
                                   float(floor(sourceFracRow))));
 
             int sourceNearestCol = int(min(
                 float(int(${s}) - 1),
-                ${o} ? float(round(sourceFracCol)) :
+                ${n} ? float(round(sourceFracCol)) :
                                   float(floor(sourceFracCol))));
 
             if (r == sourceNearestRow && c == sourceNearestCol) {
@@ -3778,17 +3797,17 @@ return a / b;`,a7=`
 
         setOutput(accumulator);
       }
-    `}};function R7(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new tC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var eF={kernelName:Pc,backendName:"webgl",kernelFunc:R7};var rC=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`
+    `}};function l9(r){let{inputs:e,backend:t,attrs:n}=r,{images:o,dy:s}=e,{alignCorners:a}=n,i=new dC(s.shape,o.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var yF={kernelName:zc,backendName:"webgl",kernelFunc:l9};var hC=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
         }
-      `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>n(l)).join(","),a=Le(o);this.userCode=`
+      `;return}let o=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>o(l)).join(","),a=Ve(n);this.userCode=`
       void main() {
         ${a} coords = getOutputCoords();
         setOutput(getX(${s}));
       }
-    `}};var oC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Wt("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Le(o);o===1?this.userCode=`
+    `}};var gC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let o=Wt("rc",n),s=`${o[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${o[n-2]} + 1 < ${this.outputShape[n-2]}`,i=Ve(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -3804,19 +3823,19 @@ return a / b;`,a7=`
         void main() {
           ${i} rc = getOutputCoords();
           vec4 result = vec4(0.);
-          result.r = ${l(n.slice())};
+          result.r = ${l(o.slice())};
           if(${s}){
-            result.g = ${u(n.slice())};
+            result.g = ${u(o.slice())};
           }
           if(${a}) {
-            result.b = ${c(n.slice())};
+            result.b = ${c(o.slice())};
             if(${s}) {
-              result.a = ${p(n.slice())};
+              result.a = ${p(o.slice())};
             }
           }
           setOutput(result);
         }
-    `;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((b,w)=>f(w,d)),g=h.join(","),x=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${x}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return jt({inputs:{x:n},backend:t});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oC(n.shape,i):new rC(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var tF={kernelName:es,backendName:"webgl",kernelFunc:F7};var nC=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let o=e[1],n=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
+    `;function l(d){return m(d)}function u(d){return d[n-1]="("+d[n-1]+" + 1)",m(d)}function c(d){return d[n-2]="("+d[n-2]+" + 1)",m(d)}function p(d){return d[n-1]="("+d[n-1]+" + 1)",d[n-2]="("+d[n-2]+" + 1)",m(d)}function m(d){let h=e.map((b,w)=>f(w,d)),g=h.join(","),y=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${y}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function u9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dims:s}=n,a=o.shape.length,i=x.parseAxisParam(s,o.shape);if(a===0)return jt({inputs:{x:o},backend:t});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new gC(o.shape,i):new hC(o.shape,i);return t.runWebGLProgram(l,[o],o.dtype)}var bF={kernelName:Qo,backendName:"webgl",kernelFunc:u9};var xC=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],o=e[2];this.outputShape=e;let s="";typeof t=="number"?s=`float outputValue = ${t.toFixed(2)};`:s=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         uniform vec4 params;
@@ -3831,12 +3850,12 @@ return a / b;`,a7=`
           int coordX = int(round(coordXFloat + params[0]));
           int coordY = int(round(coordYFloat + params[1]));
           ${s}
-          if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {
+          if(coordX >= 0 && coordX < ${o} && coordY >= 0 && coordY < ${n}) {
             outputValue = getImage(coords[0], coordY, coordX, coords[3]);
           }
           setOutput(outputValue);
         }
-    `}getCustomSetupFunc(e,t,o,n){return(s,a)=>{this.paramsLoc==null&&(this.paramsLoc=s.getUniformLocationNoThrow(a,"params")),s.gl.uniform4f(this.paramsLoc,e,t,o,n)}}};var rF={kernelName:oa,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new nC(o.shape,s),[u,c]=N.getImageCenter(a,o.shape[1],o.shape[2]),p=l.getCustomSetupFunc(u,c,Math.sin(n),Math.cos(n));return i.runWebGLProgram(l,[o],o.dtype,p)}};var O7=`
+    `}getCustomSetupFunc(e,t,n,o){return(s,a)=>{this.paramsLoc==null&&(this.paramsLoc=s.getUniformLocationNoThrow(a,"params")),s.gl.uniform4f(this.paramsLoc,e,t,n,o)}}};var wF={kernelName:aa,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:n}=r,{radians:o,fillValue:s,center:a}=e,i=t,l=new xC(n.shape,s),[u,c]=C.getImageCenter(a,n.shape[1],n.shape[2]),p=l.getCustomSetupFunc(u,c,Math.sin(o),Math.cos(o));return i.runWebGLProgram(l,[n],n.dtype,p)}};var c9=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -3851,7 +3870,7 @@ return a / b;`,a7=`
       return base + 1.0;
     }
   }
-`,P7=_e({opSnippet:O7}),oF={kernelName:ts,backendName:"webgl",kernelFunc:P7};var M7="return inversesqrt(x);",L7=_e({opSnippet:M7,cpuKernelImpl:K2}),nF={kernelName:rs,backendName:"webgl",kernelFunc:L7};var Mf=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
+`,p9=_e({opSnippet:c9}),_F={kernelName:es,backendName:"webgl",kernelFunc:p9};var m9="return inversesqrt(x);",f9=_e({opSnippet:m9,cpuKernelImpl:pD}),kF={kernelName:ts,backendName:"webgl",kernelFunc:f9};var Vf=class{constructor(e,t,n,o,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Ve(s.length),u=Ve(a.length),c="";n===1?c="i":n===2&&(c="i, j");let p=`getIndices(${c})`,m="";o===1?m="i":o===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
         ${l} strides = ${l}(${s});
 
         void main() {
@@ -3871,31 +3890,31 @@ return a / b;`,a7=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function z7(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=N.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=pe({inputs:{x:n},backend:t,attrs:{shape:[l,i]}}),d=pe({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new Mf(l,i,f.shape.length,d.shape.length,c,m),x=t.runWebGLProgram(g,[d,f,h],d.dtype),b=pe({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var sF={kernelName:Xi,backendName:"webgl",kernelFunc:z7};var sC=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);n=l.join(),s=u.join()}let a=Le(o);this.userCode=`
+      `}};function d9(r){let{inputs:e,backend:t,attrs:n}=r,{indices:o,updates:s}=e,{shape:a}=n,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=C.calculateShapes(s,o,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,o.dtype);let f=ue({inputs:{x:o},backend:t,attrs:{shape:[l,i]}}),d=ue({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new Vf(l,i,f.shape.length,d.shape.length,c,m),y=t.runWebGLProgram(g,[d,f,h],d.dtype),b=ue({inputs:{x:y},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(h),b}var vF={kernelName:ea,backendName:"webgl",kernelFunc:d9};var yC=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let o,s;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)s="resRC",o="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);o=l.join(),s=u.join()}let a=Ve(n);this.userCode=`
       void main() {
         ${a} resRC = getOutputCoords();
-        float cVal = getC(${n});
+        float cVal = getC(${o});
         if (cVal >= 1.0) {
           setOutput(getA(${s}));
         } else {
           setOutput(getB(${s}));
         }
       }
-    `}};function B7(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new sC(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],ir(n.dtype,s.dtype))}var iF={kernelName:Ws,backendName:"webgl",kernelFunc:B7};var V7=`
+    `}};function h9(r){let{inputs:e,backend:t}=r,{condition:n,t:o,e:s}=e,a=new yC(n.shape.length,o.shape,o.shape.length);return t.runWebGLProgram(a,[n,o,s],ar(o.dtype,s.dtype))}var CF={kernelName:Hs,backendName:"webgl",kernelFunc:h9};var g9=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
-  float scaleAlpha = ${N.SELU_SCALEALPHA};
-  float scale = ${N.SELU_SCALE};
+  float scaleAlpha = ${C.SELU_SCALEALPHA};
+  float scale = ${C.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,G7=_e({opSnippet:V7}),aF={kernelName:Yi,backendName:"webgl",kernelFunc:G7};var W7="return 1.0 / (1.0 + exp(-1.0 * x));",j7=_e({opSnippet:W7}),lF={kernelName:ns,backendName:"webgl",kernelFunc:j7};var U7=`
+`,x9=_e({opSnippet:g9}),IF={kernelName:ta,backendName:"webgl",kernelFunc:x9};var y9="return 1.0 / (1.0 + exp(-1.0 * x));",b9=_e({opSnippet:y9}),NF={kernelName:ns,backendName:"webgl",kernelFunc:b9};var w9=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,H7=_e({opSnippet:U7}),uF={kernelName:Ji,backendName:"webgl",kernelFunc:H7};var q7=ix+`
+`,_9=_e({opSnippet:w9}),SF={kernelName:na,backendName:"webgl",kernelFunc:_9};var k9=cx+`
   return sin(x);
-`,K7=_e({opSnippet:q7}),cF={kernelName:os,backendName:"webgl",kernelFunc:K7};var X7=`
+`,v9=_e({opSnippet:k9}),TF={kernelName:rs,backendName:"webgl",kernelFunc:v9};var C9=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,Y7=_e({opSnippet:X7}),pF={kernelName:Zi,backendName:"webgl",kernelFunc:Y7};var Z7=`
+`,I9=_e({opSnippet:C9}),AF={kernelName:ra,backendName:"webgl",kernelFunc:I9};var N9=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -3915,9 +3934,9 @@ return a / b;`,a7=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,J7=_e({opSnippet:Z7}),mF={kernelName:Qi,backendName:"webgl",kernelFunc:J7};var Q7=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((x,b)=>x*b),l=[[0,0]];l.push(...a);for(let x=1+s.length;x<n.shape.length;++x)l.push([0,0]);let u=[],c=Xv({inputs:{x:n},backend:t,attrs:{paddings:l,constantValue:0}}),p=N.getReshaped(c.shape,s,i,!1),m=N.getPermuted(p.length,s.length,!1),f=N.getReshapedPermuted(c.shape,s,i,!1),d=pe({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Mt({inputs:{x:d},backend:t,attrs:{perm:m}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(x=>t.disposeIntermediateTensorInfo(x)),g},fF={kernelName:el,backendName:"webgl",kernelFunc:Q7};function eZ(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=N.calculateShapes(s,n,i),m=!1,f=new Mf(u,l,n.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,n,a],s.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var dF={kernelName:Lc,backendName:"webgl",kernelFunc:eZ};function tZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],l=N.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),p=n.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Na({inputs:{x:n},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var hF={kernelName:Us,backendName:"webgl",kernelFunc:tZ};var rZ="return sqrt(x);",oZ=_e({opSnippet:rZ}),gF={kernelName:ss,backendName:"webgl",kernelFunc:oZ};var nZ="return x * x;",sZ=_e({opSnippet:nZ}),xF={kernelName:tl,backendName:"webgl",kernelFunc:sZ};var yF="return (a - b) * (a - b);",iZ=nt({opSnippet:yF,packedOpSnippet:yF}),bF={kernelName:ls,backendName:"webgl",kernelFunc:iZ};function aZ({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=xr+`
+`,S9=_e({opSnippet:N9}),EF={kernelName:oa,backendName:"webgl",kernelFunc:S9};var T9=r=>{let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{blockShape:s,paddings:a}=n;x.assert(o.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...a);for(let y=1+s.length;y<o.shape.length;++y)l.push([0,0]);let u=[],c=lC({inputs:{x:o},backend:t,attrs:{paddings:l,constantValue:0}}),p=C.getReshaped(c.shape,s,i,!1),m=C.getPermuted(p.length,s.length,!1),f=C.getReshapedPermuted(c.shape,s,i,!1),d=ue({inputs:{x:c},backend:t,attrs:{shape:p}}),h=$t({inputs:{x:d},backend:t,attrs:{perm:m}}),g=ue({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(y=>t.disposeIntermediateTensorInfo(y)),g},DF={kernelName:sl,backendName:"webgl",kernelFunc:T9};function A9(r){let{inputs:e,backend:t,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:a}=e,{outputShape:i}=n,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=C.calculateShapes(s,o,i),m=!1,f=new Vf(u,l,o.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,o,a],s.dtype),h=ue({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var $F={kernelName:Vc,backendName:"webgl",kernelFunc:A9};function E9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{numOrSizeSplits:s,axis:a}=n,i=x.parseAxisParam(a,o.shape)[0],l=C.prepareSplitSize(o,s,i),u=o.shape.length,c=new Array(u).fill(0),p=o.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=$a({inputs:{x:o},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var RF={kernelName:Ks,backendName:"webgl",kernelFunc:E9};var D9="return sqrt(x);",$9=_e({opSnippet:D9}),FF={kernelName:os,backendName:"webgl",kernelFunc:$9};var R9="return x * x;",F9=_e({opSnippet:R9}),OF={kernelName:il,backendName:"webgl",kernelFunc:F9};var PF="return (a - b) * (a - b);",O9=st({opSnippet:PF,packedOpSnippet:PF}),MF={kernelName:as,backendName:"webgl",kernelFunc:O9};function P9({inputs:r,attrs:e,backend:t}){let{x:n}=r,o=yr+`
     return x > 0.0 ? 1.0 : float(${e.alpha});
-  `,s=new mo(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var wF={kernelName:Xo,backendName:"webgl",kernelFunc:aZ};var iC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Le(o.length),a=Le(o.length),i="";if(n===1)i="coords * strides + begin";else{let l=0;i=o.map((u,c)=>(l++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
+  `,s=new hn(n.shape,o);return t.runWebGLProgram(s,[n],n.dtype)}var LF={kernelName:Yn,backendName:"webgl",kernelFunc:P9};var bC=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let o=n.length,s=Ve(n.length),a=Ve(n.length),i="";if(o===1)i="coords * strides + begin";else{let l=0;i=n.map((u,c)=>(l++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
       ${s} begin = ${s}(${e});
       ${s} strides = ${s}(${t});
 
@@ -3925,15 +3944,15 @@ return a / b;`,a7=`
         ${a} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function lZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=o,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:b}=er.sliceInfo(n.shape,s,a,i,l,u,c,p,m),w=pe({inputs:{x:n},backend:t,attrs:{shape:x}}),_;if(f){let D=Na({inputs:{x:w},backend:t,attrs:{begin:d,size:g}});_=pe({inputs:{x:D},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo(D)}else if(b.some(D=>D===0))_=t.makeTensorInfo(b,n.dtype,[]);else if(t.shouldExecuteOnCPU([w])){let R=t.texData.get(w.dataId).values,O=ve(w.shape,w.dtype,R),M=Y2(b,O,h,d);_=t.makeTensorInfo(b,w.dtype,M.values)}else{let T=new iC(d,h,b);_=t.runWebGLProgram(T,[w],w.dtype)}let k=pe({inputs:{x:_},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(_),k}var _F={kernelName:ea,backendName:"webgl",kernelFunc:lZ};var uZ="return tan(x);",cZ=_e({opSnippet:uZ}),kF={kernelName:ta,backendName:"webgl",kernelFunc:cZ};var pZ=`
+    `}};function M9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:y,outShape:b}=rr.sliceInfo(o.shape,s,a,i,l,u,c,p,m),w=ue({inputs:{x:o},backend:t,attrs:{shape:y}}),_;if(f){let E=$a({inputs:{x:w},backend:t,attrs:{begin:d,size:g}});_=ue({inputs:{x:E},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo(E)}else if(b.some(E=>E===0))_=t.makeTensorInfo(b,o.dtype,[]);else if(t.shouldExecuteOnCPU([w])){let D=t.texData.get(w.dataId).values,O=Ce(w.shape,w.dtype,D),M=fD(b,O,h,d);_=t.makeTensorInfo(b,w.dtype,M.values)}else{let $=new bC(d,h,b);_=t.runWebGLProgram($,[w],w.dtype)}let I=ue({inputs:{x:_},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(_),I}var zF={kernelName:sa,backendName:"webgl",kernelFunc:M9};var L9="return tan(x);",z9=_e({opSnippet:L9}),BF={kernelName:us,backendName:"webgl",kernelFunc:z9};var B9=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,mZ=_e({opSnippet:pZ}),vF={kernelName:cs,backendName:"webgl",kernelFunc:mZ};var aC=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[a]*t[a];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=fZ(e);this.userCode=`
+`,V9=_e({opSnippet:B9}),VF={kernelName:cs,backendName:"webgl",kernelFunc:V9};var wC=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let o=Ve(this.rank),s=G9(e);this.userCode=`
       void main() {
-        ${n} resRC = getOutputCoords();
+        ${o} resRC = getOutputCoords();
         setOutput(getA(${s}));
       }
-    `}};function fZ(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n<r.length;n++)o.push(`imod(${t[n]}, ${r[n]})`);return o.join()}function lC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(n.dtype==="string"){let u=t.readSync(n.dataId).map(m=>y.decodeString(m)),c=ve(n.shape,n.dtype,u),p=J2(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new aC(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var CF={kernelName:Oo,backendName:"webgl",kernelFunc:lC};function dZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=t.readSync(n.dataId),[l,u]=Q2(i,n.shape,n.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var IF={kernelName:ra,backendName:"webgl",kernelFunc:dZ};var uC=class{constructor(e,t,o,n,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=o==="nearest"?1:2,l;switch(n){case"constant":l=1;break;case"reflect":l=2;break;case"wrap":l=3;break;case"nearest":l=4;break;default:l=1;break}this.userCode=`
+    `}};function G9(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let o=0;o<r.length;o++)n.push(`imod(${t[o]}, ${r[o]})`);return n.join()}function _C(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reps:s}=n;if(o.dtype==="string"){let u=t.readSync(o.dataId).map(m=>x.decodeString(m)),c=Ce(o.shape,o.dtype,u),p=hD(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new wC(o.shape,s);return t.runWebGLProgram(a,[o],o.dtype)}var GF={kernelName:Pn,backendName:"webgl",kernelFunc:_C};function W9(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{k:s,sorted:a}=n,i=t.readSync(o.dataId),[l,u]=gD(i,o.shape,o.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var WF={kernelName:ia,backendName:"webgl",kernelFunc:W9};var kC=class{constructor(e,t,n,o,s,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,l;switch(o){case"constant":l=1;break;case"reflect":l=2;break;case"wrap":l=3;break;case"nearest":l=4;break;default:l=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${l} == 2) {
@@ -4045,13 +4064,13 @@ return a / b;`,a7=`
               }
               setOutput(outputValue);
             }
-        `}};function hZ(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:l,outputShape:u}=o,[c,p,m,f]=n.shape,[d,h]=u!=null?u:[p,m],g=[c,d,h,f],x=new uC(p,m,a,i,l,g);return t.runWebGLProgram(x,[n,s],"float32")}var NF={kernelName:zc,backendName:"webgl",kernelFunc:hZ};function gZ(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;_s(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=eD(a,n,s.shape,s.dtype);return[o.makeTensorInfo(l,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var SF={kernelName:Bc,backendName:"webgl",kernelFunc:gZ};function xZ(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,l=n.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=Na({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),x=pe({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=x,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var TF={kernelName:Hs,backendName:"webgl",kernelFunc:xZ};var cC=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let l="0.0",u="sumValue",c=Math.floor(o/4)*4,p=o%4,m=`
+        `}};function j9(r){let{inputs:e,backend:t,attrs:n}=r,{image:o,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:l,outputShape:u}=n,[c,p,m,f]=o.shape,[d,h]=u!=null?u:[p,m],g=[c,d,h,f],y=new kC(p,m,a,i,l,g);return t.runWebGLProgram(y,[o,s],"float32")}var jF={kernelName:Gc,backendName:"webgl",kernelFunc:j9};function U9(r){let{inputs:e,attrs:t,backend:n}=r,{axis:o}=t,{x:s}=e;ks(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=n.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=xD(a,o,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,i),n.makeTensorInfo([u.length],"int32",u)]}var UF={kernelName:Wc,backendName:"webgl",kernelFunc:U9};function H9(r){let{inputs:e,backend:t,attrs:n}=r,{value:o}=e,{axis:s}=n;s<0&&(s+=o.shape.length);let a=o,i=a.shape.length,l=o.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=$a({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),y=ue({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=y,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var HF={kernelName:Xs,backendName:"webgl",kernelFunc:H9};var vC=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,o=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/n);this.outputShape=[o,i];let l="0.0",u="sumValue",c=Math.floor(n/4)*4,p=n%4,m=`
         sumValue += dot(values, segFilter);
-    `,f="";s%o>0&&(f=`
+    `,f="";s%n>0&&(f=`
         if (inIdx < 0 || inIdx >= ${s}) {
           return initializationValue;
         }
-      `);let d="";s%o>0&&(d=`
+      `);let d="";s%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${s}) {
           return -1.0;
         }
@@ -4073,7 +4092,7 @@ return a / b;`,a7=`
         int batch = coords[0];
         int outIdx = coords[1];
         int inOffset = int(floor(float(outIdx) / float(
-          ${a})) * float(${o}));
+          ${a})) * float(${n}));
         int currentSeg = int(mod(float(outIdx), float(${a})));
 
         float sumValue = 0.0;
@@ -4151,19 +4170,19 @@ return a / b;`,a7=`
         }
         setOutput(${u});
       }
-    `}};function yZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,l=[],u=0,c=N.getAxesPermutation([u],i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),l.push(p),u=N.getInnerMostAxes(1,i)[0]);let m=N.segment_util.computeOutShape(p.shape,u,a),f=y.sizeFromShape([p.shape[u]]),d=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=Gl(n.dtype),g=(_,k,D,T,R)=>{let O=_.shape[0],M=_.shape[1],G=N.segment_util.segOpComputeOptimalWindowSize(M,R),j={windowSize:G,inSize:M,batchSize:O,numSegments:R},U=new cC(j,k),H=t.compileAndRun(U,[_,D],T);if(l.push(H),H.shape[1]===R)return H;let q=Yv({backend:t,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),X=lC({inputs:{x:q},backend:t,attrs:{reps:[M/G]}});return l.push(q),l.push(X),g(H,k,X,T,R)},x=g(d,"unsortedSegmentSum",s,h,a),b=pe({inputs:{x},backend:t,attrs:{shape:m}}),w=b;if(c!=null){l.push(b);let _=N.getUndoAxesPermutation(c);w=Mt({inputs:{x:w},backend:t,attrs:{perm:_}})}return 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wZ(r){EF=r.wasm.cwrap(Ks,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function _Z(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o,m=t.dataIdMap.get(n.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);d=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=bl[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let x=l?n.shape[2]:n.shape[1],b=u?s.shape[1]:s.shape[2],w=n.shape[0],_=t.makeOutput([w,x,b],n.dtype),k=t.dataIdMap.get(_.dataId).id,D=new Uint8Array(new Int32Array(n.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return EF(m,D,n.shape.length,f,T,s.shape.length,l,u,g,d,h,p||0,k),_}var DF={kernelName:Ks,backendName:"wasm",setupFunc:wZ,kernelFunc:_Z};function Nt(r){let e;function t(n){e=n.wasm.cwrap(r,null,["number","number"])}function o(n){let{backend:s,inputs:{x:a}}=n,i=s.dataIdMap.get(a.dataId).id,l=s.makeOutput(a.shape,a.dtype),u=s.dataIdMap.get(l.dataId).id;return y.sizeFromShape(l.shape)===0||e(i,u),l}return{kernelName:r,backendName:"wasm",setupFunc:t,kernelFunc:o}}var $F=Nt(Os);function xt(r,e,t){let o;function n(a){o=a.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(a){let{backend:i,inputs:l}=a,{a:u,b:c}=l,p=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,f=t!=null?t:u.dtype,d=N.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(d,f);if(y.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),x=new Uint8Array(new 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Fu(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var PF={kernelName:Ko,backendName:"wasm",kernelFunc:Fu};var MF;function IZ(r){MF=r.wasm.cwrap(ps,null,["number","array","number","number","number","array","number"])}function Sp(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=SZ(e.x.shape,o.perm),a=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(a=!1);let i=NZ(e.x.shape,o.perm),l={dataId:e.x.dataId,shape:n,dtype:e.x.dtype};if(a){let d=Fu({inputs:e,backend:t});return d.shape=i,d}let u=t.makeOutput(i,l.dtype),c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,m=new Uint8Array(new Int32Array(s).buffer),f=new Uint8Array(new Int32Array(l.shape).buffer);return MF(c,f,l.shape.length,Lt[l.dtype],p,m,s.length),u}function NZ(r,e){let t=new Array(r.length);for(let o=0;o<t.length;o++)t[o]=r[e[o]];return t}function SZ(r,e){let t=[],o=[];for(let n=0;n<r.length;++n)r[n]!==1&&t.push(r[n]),r[e[n]]!==1&&o.push(e[n]);for(let n=0;n<o.length;++n){let s=-1;for(let a=0;a<o.length;++a)o[a]>=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var LF={kernelName:ps,backendName:"wasm",kernelFunc:Sp,setupFunc:IZ};function pn(r,e,t){let o=r.shape,n=r.shape.length,s=y.parseAxisParam(e,o),a=s,i=N.getAxesPermutation(a,n),l=null,u=!1;if(i!=null){let c=new Array(n);for(let f=0;f<c.length;f++)c[f]=o[i[f]];a=N.getInnerMostAxes(a.length,n),l=Sp({inputs:{x:r},attrs:{perm:i},backend:t});let p=t.dataIdMap.get(r.dataId).id;t.dataIdMap.get(l.dataId).id!==p&&(u=!0)}return{transposed:l,originalAxes:s,axes:a,inputWasTransposed:u}}var zF;function TZ(r){zF=r.wasm.cwrap(vn,null,["number","number","number","number","number"])}function AZ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n}=o,{x:s}=t,a=e.dataIdMap.get(s.dataId).id,i=a,l=s,{transposed:u,axes:c,inputWasTransposed:p}=pn(s,n,e);if(p){let x=e.dataIdMap.get(u.dataId).id;x!==a&&(l=u,i=x)}let m=l.shape.slice(0,-1),f=e.makeOutput(m,"int32"),d=e.dataIdMap.get(f.dataId).id,h=y.sizeFromShape(f.shape),g=l.shape[c[0]];return zF(i,Lt[l.dtype],h,g,d),p&&e.disposeData(u.dataId),f}var BF={kernelName:vn,backendName:"wasm",kernelFunc:AZ,setupFunc:TZ};var VF;function EZ(r){VF=r.wasm.cwrap(Cn,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function DZ(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id,{filterSize:a,strides:i,pad:l,dimRoundingMode:u}=t,c=N.computePool2DInfo(n.shape,a,i,1,l,u),p=c.filterHeight,m=c.filterWidth,f=c.padInfo.top,d=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.strideHeight,b=c.strideWidth,w=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let _=o.makeOutput(c.outShape,"float32"),k=o.dataIdMap.get(_.dataId).id;return VF(s,n.shape[0],n.shape[1],n.shape[2],p,m,f,d,h,g,x,b,w,k),_}var GF={kernelName:Cn,backendName:"wasm",setupFunc:EZ,kernelFunc:DZ};function Br(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s);return y.assert(s===y.sizeFromShape(a),()=>`new shape: ${a}, old shape: ${o.shape}. 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Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let w=b.sourceLayer,_=b.nodeIndex,k=b.tensorIndex;this.outputLayers.push(w),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(k)}for(let b of this.inputs){let w=b.sourceLayer,_=b.nodeIndex,k=b.tensorIndex;Vo(_===0,"input layer has >1 nodes"),Vo(k===0,"input layer has >1 tensors"),this.inputLayers.push(w),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(k)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let w=this.inputLayers[b];if(!(w instanceof ui))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${w.getClassName()}.`);this.inputNames.push(w.name),this.feedInputShapes.push(w.batchInputShape),this.feedInputNames.push(w.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},o={},n={},s={},a={},i=[],l=(b,w,_,k,D,T)=>{(k==null||D==null||T==null)&&(k=b.sourceLayer,D=b.nodeIndex,T=b.tensorIndex);let R=k.inboundNodes[D];if(_.indexOf(R)!==-1)throw new Vr(`The tensor ${b.name} at layer "${k.name}" is part of a cycle.`);if(w.indexOf(R)!==-1)return;this.containerNodes.add(Go.nodeKey(k,D)),k.id in a||(a[k.id]=Object.keys(a).length),_.indexOf(R)===-1&&_.push(R);let O=R.inboundLayers.length;for(let M=0;M<O;M++){let G=R.inputTensors[M],j=R.inboundLayers[M],U=R.nodeIndices[M],H=R.tensorIndices[M];l(G,w,_,j,U,H)}for(w.push(R);_.indexOf(R)>=0;)_.splice(_.indexOf(R),1);i.push(R)},u=[],c=[];for(let b of this.outputs)l(b,u,c);let p=i.slice().reverse();for(let b of p){o[b.id]=b,b.id in t||(t[b.id]=0);let w=t[b.id],_=n[b.outboundLayer.id]==null?0:n[b.outboundLayer.id];w=Math.max(w,_),n[b.outboundLayer.id]=w,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=w;for(let k=0;k<b.inboundLayers.length;k++){let D=b.inboundLayers[k],T=b.nodeIndices[k],R=D.inboundNodes[T],O=t[R.id]==null?0:t[R.id];t[R.id]=Math.max(w+1,O),o[R.id]=R}}let m={};for(let b in t){let w=t[b];w in m||(m[w]=[]),m[w].push(o[b])}let f={};for(let b in n){let w=n[b];w in f||(f[w]=[]),f[w].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(Gf);this.layers=[];for(let b of d){let w=f[b];w.sort((_,k)=>{let D=a[_.id],T=a[k.id];return D<T?-1:D>T?1:0});for(let _ of w)_ instanceof Go&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(Gf);let h=this.inputs.slice(),g=[];for(let b of d)for(let w of m[b]){let _=w.outboundLayer;if(_!=null){for(let k of w.inputTensors)if(h.indexOf(k)===-1)throw new Vr(`Graph disconnected: cannot obtain value for tensor ${k} at layer "${_.name}". 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.layers)t.push(...o.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let o={},n=0;for(let a of this.layers)for(let i of a.weights){if(o[i.originalName]!=null)throw new z(`Duplicate weight name: ${i.originalName}`);o[i.originalName]=i,n++}let s=[];for(let a in e){let i=a;if(o[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(o[i]!=null)s.push([o[i],e[a]]);else if(t)throw new z(`Provided weight data has no target variable: ${a}`);delete o[i]}if(t){let a=[];for(let i in o)a.push(i);if(a.length>0)throw new z(`${a.length} of ${n} weights are not set: ${a}`)}Wp(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${cd}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let o=Ux(this.updatedConfig());return t?JSON.stringify(o):o}call(e,t){return V(()=>{e=yt(e);let o=new Ss;for(let n=0;n<this.inputs.length;++n)o.add(this.inputs[n],e[n]);return Gu(this.outputs,o,t)})}computeMask(e,t){return V(()=>{e=yt(e);let o;return t==null?o=mn(null,e.length):o=yt(t),this.runInternalGraph(e,o)[1]})}computeOutputShape(e){let t=Vp(e);if(t.length!==this.inputLayers.length)throw new z(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let o={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";o[c]=u}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Gf);if(n.length>1)for(let i of n){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],x=u.nodeIndices[h],b=u.tensorIndices[h],w=`${g.name}_${x}_${b}`,_=o[w];p.push(_)}let m=c.computeOutputShape(yr(p)),f=Vp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;o[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];Vo(l in o),s.push(o[l])}return yr(s)}runInternalGraph(e,t){t==null&&(t=mn(null,e.length));let o={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];o[u.id]=[c,p]}let n=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(Gf);for(let l of n){let u=this.nodesByDepth[l];for(let c of u){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in o&&d.push(o[h.id]);if(d.length===m.length){let h={},g,x,b,w;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[_,k]=d[0];h.mask==null&&(h.mask=k),b=yt(p.call(_,h)),w=yt(p.computeMask(_,k)),g=[_],x=[k]}else g=d.map(_=>_[0]),x=d.map(_=>_[1]),h.mask==null&&(h.mask=x),b=yt(p.call(g,h)),w=yt(p.computeMask(g,x));if(p.activityRegularizer)throw new Se("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<f.length;++_){let k=f[_],D=b[_],T=w[_];o[k.id]=[D,T]}}}}let s=[],a=[],i=[];for(let l of this.outputs){Vo(l.id in o,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=o[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},o;for(let n of this.layers){o=n instanceof Go?1:0;for(let s=0;s<n.inboundNodes.length;s++){let a=Go.nodeKey(n,s);this.containerNodes.has(a)&&(t[a]=o,o+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new z(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new z("Provide either a layer name or layer index");for(let o of this.layers)if(o.name===e)return o;throw new z(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let o=0;o<t.inboundNodes.length;++o){let n=Go.nodeKey(t,o);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),o=[];for(let a of this.layers){let i=a.getClassName(),l=a.getConfig(),u=[];for(let p=0;p<a.inboundNodes.length;p++){let m=a.inboundNodes[p],f=Go.nodeKey(a,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${m.callArgs}. 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Provided ${t} not understood: ${JSON.stringify(r)}`)}function Hx(r,e){return WQ(r,e,"classWeight")}async function qx(r,e,t,o){if(e!=null||o!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let n=V(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());Ae(n);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let n=t.validationData!=null,s,a;if(n)if(Wz(t.validationData))y.assert(t.validationBatches==null||t.validationBatches>0&&Number.isInteger(t.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${t.validationBatches}`);else{let g=UQ(t.validationData);s=g.xs,a=g.ys}let i=r.makeTrainFunction(),l=r.getDedupedMetricsNames(),u;n?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=Lx(t.callbacks,t.yieldEvery),p=t.verbose==null?1:t.verbose,{callbackList:m,history:f}=zx(c,p,t.epochs,null,null,HQ(e,t),null,n,u);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=t.initialEpoch==null?0:t.initialEpoch,h=await e.iterator();for(;d<t.epochs;){let g={};await m.onEpochBegin(d);let x=0,b=0;for(o||(h=await e.iterator());o?x<t.batchesPerEpoch:!0;){let w=await h.next();if(o&&w.done){console.warn(`You provided \`batchesPerEpoch\` as ${t.batchesPerEpoch}, but your dataset iterator ran out of data after ${x} batches; interrupting training. 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instead`);if(s==="channelsFirst"&&(r=Ke(r,[0,2,1])),n==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=Yl(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=ho(i,t)),i})}function r3(r,e,t,o=[1,1],n="valid",s,a,i=null){return V(()=>{if(s==null&&(s=to()),$t(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=bd(r,s);if(n==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=rn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ke(l,[0,3,1,2])),l})}function aee(r,e,t,o=[1,1,1],n="valid",s,a){return V(()=>{if(s==null&&(s=to()),$t(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=a0(r,s);if(n==="causal")throw new Se("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=jm(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ho(i,t)),s==="channelsFirst"&&(i=Ke(i,[0,4,1,2,3])),i})}var Kp=class extends Me{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Kp.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Se(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Nl(t.kernelSize,e,"kernelSize"),this.strides=Nl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ro(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,$t(this.dataFormat),this.activation=As(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ot(t.biasConstraint),this.biasRegularizer=bt(t.biasRegularizer),this.activityRegularizer=bt(t.activityRegularizer),this.dilationRate=Nl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Vo("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Ix(e.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ts(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),biasConstraint:Ft(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ju=class extends Kp{constructor(e,t){super(e,t);this.kernel=null,ju.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ot(t.kernelConstraint),this.kernelRegularizer=bt(t.kernelRegularizer)}build(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let o=e[t],n=this.kernelSize.concat([o,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:o}}],this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o,n=this.bias==null?null:this.bias.read(),s=Nx(this.activation.getClassName());if(s!=null&&this.rank===2)o=r3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)o=iee(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)o=r3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)o=aee(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Se("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(o=this.activation.apply(o))}return o})}computeOutputShape(e){e=Je(e);let t=[],o=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<o.length;++s){let a=bo(o[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:st(this.kernelRegularizer),kernelConstraint:Ft(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Sl=class extends ju{constructor(e){super(2,e);Sl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ix(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Sl.className="Conv2D";Q.registerClass(Sl);var Uu=class extends ju{constructor(e){super(3,e);Uu.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Uu.className="Conv3D";Q.registerClass(Uu);var wd=class extends Sl{constructor(e){super(e);if(this.inputSpec=[new At({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Je(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new At({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{let o=Fe(e);if(o.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=yd(l,m,c,this.padding),h=yd(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=Ke(o,[0,2,3,1]));let x=Zl(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(x=Ke(x,[0,3,1,2])),this.bias!=null&&(x=ho(x,this.bias.read(),this.dataFormat)),this.activation!=null&&(x=this.activation.apply(x)),x})}computeOutputShape(e){e=Je(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=yd(t[n],l,a,this.padding),t[s]=yd(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};wd.className="Conv2DTranspose";Q.registerClass(wd);var l0=class extends ju{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=bt(t.depthwiseRegularizer),this.depthwiseConstraint=Ot(t.depthwiseConstraint),this.pointwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=bt(t.pointwiseRegularizer),this.pointwiseConstraint=Ot(t.pointwiseConstraint)}build(e){if(e=Je(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let o=e[t],n=this.kernelSize.concat([o,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(o*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new At({ndim:this.rank+2,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o;if(this.rank===1)throw new Se("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ke(e,[0,2,3,1])),o=nf(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=ho(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=Ke(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.pointwiseRegularizer=st(this.pointwiseRegularizer),e.depthwiseConstraint=Ft(this.depthwiseConstraint),e.pointwiseConstraint=Ft(this.pointwiseConstraint),e}};l0.className="SeparableConv";var _d=class extends l0{constructor(e){super(2,e)}};_d.className="SeparableConv2D";Q.registerClass(_d);var Hu=class extends ju{constructor(e){super(1,e);Hu.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ix(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Hu.className="Conv1D";Q.registerClass(Hu);var kd=class extends Me{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Fe(e),this.dataFormat==="channelsLast"){let o=Kf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Kf(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=Kf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Kf(o,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};kd.className="Cropping2D";Q.registerClass(kd);var vd=class extends Me{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,mz(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return V(()=>{let o=Fe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=Ke(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return Ke(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};vd.className="UpSampling2D";Q.registerClass(vd);function lee(r,e,t=[1,1],o="valid",n,s){return V(()=>{n==null&&(n=to()),$t(n);let a=bd(r,n);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=ds(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=Ke(a,[0,3,1,2])),a})}var Cd=class extends Kp{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ot(e.depthwiseConstraint),this.depthwiseRegularizer=bt(e.depthwiseRegularizer)}build(e){if(e=Je(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o=lee(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=ho(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=bo(t,this.kernelSize[0],this.padding,this.strides[0]),a=bo(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.depthwiseConstraint=Ft(this.depthwiseRegularizer),e}};Cd.className="DepthwiseConv2D";Q.registerClass(Cd);function u0(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function c0(r,e,t,o=!1,n,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Gr(2,l));if(e=Ke(e,u),s!=null)throw new Se("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=lr(n,-1)),n=Ke(n,u)),o&&(e=qt(e,0),n!=null&&(n=qt(n,0)));let c=[],p,m=t,f=e.shape[0],d=mr(e),h;n!=null&&(h=mr(n));for(let x=0;x<f;++x){let b=d[x],w=V(()=>r(b,m));if(n==null)p=w[0],m=w[1];else{let _=V(()=>{let k=h[x],D=rr(k).sub(k),T=w[0].mul(k).add(m[0].mul(D)),R=m.map((O,M)=>w[1][M].mul(k).add(O.mul(D)));return{output:T,newStates:R}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=Bt(c,1)),[p,g,m]})}var Eo=class extends Me{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Xp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new At({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Gr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Fx(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;o<e;++o)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Se("Constants support is not implemented in RNN yet.");Fx(e)&&(e=e[0]),e=e;let o=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new At({shape:[o,null,...n]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Se("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!y.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new At({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Ao("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>ht([o,n])):this.states_=[ht([o,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>ht([o,n])):this.states_[0]=ht([o,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let n=0;n<this.states_.length;++n){let s=e[n],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[o,a];if(!y.arraysEqual(s.shape,i))throw new z(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[n]=s}}this.states_=this.states_.map(n=>Et(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=u0(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new At({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof oo){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;e=Fe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new z(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},u=c0((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=ht(e.shape);return t=ge(t,[1,2]),t=Ea(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?Ax(t,[1,o]):t):this.cell.stateSize>1?[Ax(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let o=this.cell.getConfig();return this.getClassName()===Eo.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=no(n,o);return new e(Object.assign(t,{cell:s}))}};Eo.className="RNN";Q.registerClass(Eo);var Tl=class extends Me{},Yp=class extends Tl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=As(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Mu([1,Ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Mu([1,Ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let o=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ra({ones:()=>rr(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ra({ones:()=>rr(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=xn(P(e,a),this.kernel.read()):s=xn(e,this.kernel.read()),this.bias!=null&&(s=ho(s,this.bias.read())),i!=null&&(o=P(o,i));let l=ee(s,xn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ts(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Yp.className="SimpleRNNCell";Q.registerClass(Yp);var Id=class extends Eo{constructor(e){e.cell=new Yp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};Id.className="SimpleRNN";Q.registerClass(Id);var Zp=class extends Tl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=As(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=As(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Mu([1,Ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Mu([1,Ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Je(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ra({ones:()=>rr(e),rate:this.dropout,training:o,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ra({ones:()=>rr(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let c=xn(e,this.kernel.read());this.useBias&&(c=ho(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=P(n,a[0]));let p=this.recurrentKernel.read(),[m,f]=pr(p,[2*this.units,this.units],p.rank-1),d=xn(n,m),[h,g,x]=pr(c,3,c.rank-1),[b,w]=pr(d,2,d.rank-1);i=this.recurrentActivation.apply(ee(h,b)),l=this.recurrentActivation.apply(ee(g,w));let _=xn(P(l,n),f);u=this.activation.apply(ee(x,_));let k=ee(P(i,n),P(ee(1,He(i)),u));return[k,k]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ts(this.activation),recurrentActivation:Ts(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Zp.className="GRUCell";Q.registerClass(Zp);var Nd=class extends Eo{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Zp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Nd.className="GRU";Q.registerClass(Nd);var Al=class extends Tl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=As(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=As(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=bt(e.kernelRegularizer),this.recurrentRegularizer=bt(e.recurrentRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.kernelConstraint=Ot(e.kernelConstraint),this.recurrentConstraint=Ot(e.recurrentConstraint),this.biasConstraint=Ot(e.biasConstraint),this.dropout=Mu([1,Ns([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Mu([1,Ns([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Je(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends go{apply(l,u){let c=s.apply([a]),p=new zu().apply([a]),m=s.apply([a*2]);return DC(DC(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ra({ones:()=>rr(e),rate:this.dropout,training:o,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ra({ones:()=>rr(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let m=xn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=P(n,i[0])),m=ee(m,xn(n,this.recurrentKernel.read())),this.useBias&&(m=ho(m,this.bias.read()));let[f,d,h,g]=pr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=ee(P(u,s),P(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let x=P(p,this.activation.apply(c));return[x,x,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ts(this.activation),recurrentActivation:Ts(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),recurrentConstraint:Ft(this.recurrentConstraint),biasConstraint:Ft(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Al.className="LSTMCell";Q.registerClass(Al);var Sd=class extends Eo{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Al(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Sd.className="LSTM";Q.registerClass(Sd);var Xp=class extends Tl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];o=n[i],i===0?a=[e[0]].concat(o):a=[a[0]].concat(o),a=l.call(a,t),s.push(a.slice(1))}o=[];for(let i of s.slice().reverse())o.push(...i);return[a[0]].concat(o)})}build(e){Fx(e)&&(e=e[0]),e=e;let t;this.cells.forEach((o,n)=>{Is(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(no(s,o));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return rd(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;a<o.weights.length;++a)t.push([o.weights[a],s[a]])}Wp(t)}};Xp.className="StackedRNNCells";Q.registerClass(Xp);function Ra(r){let{ones:e,rate:t,training:o=!1,count:n=1}=r,s=()=>Dx(e(),t),a=()=>wl(s,e,o);return!n||n<=1?Et(a().clone()):Array(n).fill(void 0).map(a).map(l=>Et(l.clone()))}var uee=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n<o.length;n++)e.indexOf(o[n])<0&&Object.prototype.propertyIsEnumerable.call(r,o[n])&&(t[o[n]]=r[o[n]]);return t};var p0=class extends Eo{constructor(e){if(e.unroll)throw new Se("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Se("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new At({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)],a=ht(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Ao("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ht(s)):this.states_=[ht(s)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ht(s)):this.states_[0]=ht(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!y.arraysEqual(l.shape,u))throw new z(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Et(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=bo(u,n[0],s,a[0],i[0]),m=bo(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};p0.className="ConvRNN2D";var Jp=class extends Al{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ut(this.filters,"filters"),this.kernelSize=Nl(o,2,"kernelSize"),this.kernelSize.forEach(l=>Ut(l,"kernelSize")),this.strides=Nl(n||1,2,"strides"),this.strides.forEach(l=>Ut(l,"strides")),this.padding=s||"valid",ro(this.padding),this.dataFormat=a||"channelsLast",$t(this.dataFormat),this.dilationRate=Nl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Ut(l,"dilationRate"))}build(e){var t;e=Je(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends go{apply(m,f){let d=u.apply([c]),h=Ar([c]),g=u.apply([c*2]);return Rp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ra({ones:()=>rr(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(J,ie,ue)=>!ie||!ie[ue]?J:P(ie[ue],J),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ra({ones:()=>rr(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),x=u(s,d,2),b=u(s,d,3),w=3,[_,k,D,T]=pr(this.kernel.read(),i,w),[R,O,M,G]=this.useBias?pr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,R,this.padding),p=this.inputConv(p,k,O,this.padding),m=this.inputConv(m,D,M,this.padding),f=this.inputConv(f,T,G,this.padding);let[j,U,H,q]=pr(this.recurrentKernel.read(),i,w);h=this.recurrentConv(h,j),g=this.recurrentConv(g,U),x=this.recurrentConv(x,H),b=this.recurrentConv(b,q);let X=this.recurrentActivation.apply(ee(c,h)),oe=this.recurrentActivation.apply(ee(p,g)),Y=ee(P(oe,a),P(X,this.activation.apply(ee(m,x)))),re=P(this.recurrentActivation.apply(ee(f,b)),this.activation.apply(Y));return[re,re,Y]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=uee(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=Jr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?ho(s,o,this.dataFormat):s}recurrentConv(e,t){return Jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Jp.className="ConvLSTM2DCell";Q.registerClass(Jp);var Td=class extends p0{constructor(e){let t=new Jp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Td.className="ConvLSTM2D";Q.registerClass(Td);var Qp=class extends Me{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,o=[];for(let n=0;n<this.noiseShape.length;++n)o.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return o}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,s=this.getNoiseShape(o);return wl(()=>Dx(o,this.rate,s,this.seed),()=>o,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Qp.className="Dropout";Q.registerClass(Qp);var Ad=class extends Qp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Ad.className="SpatialDropout1D";Q.registerClass(Ad);var Ed=class extends Me{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ut(this.units,"units"),this.activation=As(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ot(e.kernelConstraint),this.biasConstraint=Ot(e.biasConstraint),this.kernelRegularizer=bt(e.kernelRegularizer),this.biasRegularizer=bt(e.biasRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Je(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Je(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=Nx(this.activation.getClassName()),s;return n!=null?s=xn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=xn(o,this.kernel.read()),this.bias!=null&&(s=ho(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Ts(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Ft(this.kernelConstraint),biasConstraint:Ft(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ed.className="Dense";Q.registerClass(Ed);var Dd=class extends Me{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Je(e);for(let t of e.slice(1))if(t==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],gn(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s<o.rank;++s)n.push(s);n.push(1),o=o.transpose(n)}return bz(o)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Dd.className="Flatten";Q.registerClass(Dd);var $d=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.activation=As(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Ts(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};$d.className="Activation";Q.registerClass($d);var Rd=class extends Me{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Fe(e),xz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Rd.className="RepeatVector";Q.registerClass(Rd);var Fd=class extends Me{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let o="Total size of new array must be unchanged.",n=t.slice(),s=1,a=null;for(let l=0;l<n.length;++l){let u=n[l];if(this.isUnknown(u))if(a===null)a=l;else throw new z("Can only specifiy one unknown dimension.");else s*=u}let i=gn(e);if(a!==null){if(s===0||i%s!=0)throw new z(o);n[a]=i/s}else if(i!==s)throw new z(o);return n}computeOutputShape(e){let t=!1;for(let o=0;o<e.length;++o)if(this.isUnknown(e[o])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Fd.className="Reshape";Q.registerClass(Fd);var Od=class extends Me{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Gr(1,e.dims.length+1);if(!y.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new At({ndim:this.dims.length+1})]}computeOutputShape(e){e=Je(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),t}call(e,t){return Ke(Fe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Od.className="Permute";Q.registerClass(Od);var Pd=class extends Me{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let o=Fe(e),n=-1;return al(tn(o,this.maskValue),n)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=-1,s=!0,a=al(tn(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};Pd.className="Masking";Q.registerClass(Pd);var Md=class extends Me{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(yt(e.inputLength))}this.inputDim=e.inputDim,Ut(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ut(this.outputDim,"outputDim"),this.embeddingsInitializer=pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=bt(e.embeddingsRegularizer),this.activityRegularizer=bt(e.activityRegularizer),this.embeddingsConstraint=Ot(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return V(()=>this.maskZero?(e=Fe(e),tn(e,Ce(e))):null)}computeOutputShape(e){if(e=Je(e),this.inputLength==null)return[...e,this.outputDim];let t=yt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n<t.length;++n){let s=t[n],a=e[n+1];if(s!=null&&a!=null&&s!==a)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[o]=a),o++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return o.dtype!=="int32"&&(o=Aa(o,"int32")),Ex(this.embeddings.read(),o.as1D()).reshape(Je(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:st(this.embeddingsRegularizer),activityRegularizer:st(this.activityRegularizer),embeddingsConstraint:Ft(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Md.className="Embedding";Q.registerClass(Md);var El=class extends Me{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Se}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let o=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let s=e[e.length-t.length+n],a=t[n];if(s==null||a==null||s<0||a<0)o.push(null);else if(s===1)o.push(a);else if(a===1)o.push(s);else{if(s!==a)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));o.push(s)}}return o}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Je(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=hn(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);o=this.computeElementwiseOpOutputShape(o,a)}let n=e.map(s=>s.length);e.indexOf(null)===-1&&hn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=Ns(n);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=Ea(a,1);o.push(a)}return this.mergeFunction(o)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(gn(c.slice(1))));f=Ke(f,[1,0]),f=f.reshape(m),o.push(f),s=!0}else if(u>1){let c=Gr(1,u).concat([0]);o.push(Ke(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=Ke(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Gr(0,i-1));a=Ke(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let o=[];for(let n of e)n!=null&&n[0]!==null&&o.push(n[0]);return o=hn(o),o.length===1?t=o.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:lr(n,0));let o=t[0];for(let n=1;n<t.length-1;++n)o=gr(o,t[n]);return o})}},Ld=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=ee(t,e[o]);return t})}};Ld.className="Add";Q.registerClass(Ld);var zd=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=P(t,e[o]);return t})}};zd.className="Multiply";Q.registerClass(zd);var Bd=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=ee(t,e[o]);return P(1/e.length,t)})}};Bd.className="Average";Q.registerClass(Bd);var Vd=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=eo(t,e[o]);return t})}};Vd.className="Maximum";Q.registerClass(Vd);var Gd=class extends El{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=ys(t,e[o]);return t})}};Gd.className="Minimum";Q.registerClass(Gd);var Wd=class extends El{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let o=[];for(let n=0;n<e.length;++n){let s=e[n].slice();s.splice(this.axis,1);let a=!1;for(let i of o)if(y.arraysEqual(i,s)){a=!0;break}a||o.push(s)}if(o.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Wd.className="Concatenate";Q.registerClass(Wd);function jd(r,e){for(;r<0;)r+=e;return r}function cee(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Se("batchDot is not implemented for tensors of 4D or higher rank yet");if(y.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),y.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Se("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return V(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;o>n?l=o+n-3:l=o-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var Ud=class extends El{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){y.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],o=e[1];if(t.length>3||o.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new z(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>jd(s,e[a].shape.length)):n=[jd(this.axes,t.shape.length),jd(this.axes,o.shape.length)],this.normalize&&(t=od(t,n[0]),o=od(o,n[1])),cee(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[jd(this.axes,e.length),jd(this.axes,t.length)],o}computeOutputShape(e){y.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),o=e[1].slice();if(t.length>3||o.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ud.className="Dot";Q.registerClass(Ud);var Hd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return wl(()=>Fp(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};Hd.className="GaussianNoise";Q.registerClass(Hd);var qd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return this.rate>0&&this.rate<1?wl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(Fp(o.shape,1,s))},()=>o,t.training||!1):o})}};qd.className="GaussianDropout";Q.registerClass(qd);var Kd=class extends Me{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Fe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let o=this._getNoiseShape(e);return wl(()=>{let s=Fe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=po(bs(o),this.rate);u=Aa(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Fe(e),t.training||!1)}return e})}};Kd.className="AlphaDropout";Q.registerClass(Kd);function Xd(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=Sw(r,e,t,o,n,s);else if(r.rank===3)a=Tw(r,e,t,o,n,s);else if(r.rank===4)a=Aw(r,e,t,o,n,s);else throw new Se(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function pee(r,e,t,o,n=.001){return V(()=>{let s=Qc(r,o),a=s.mean,i=s.variance;return[Xd(r,a,i,t,e,n),a,i]})}function mee(r,e,t,o,n=.001){return V(()=>{let s=Qc(r,o),a=s.mean,i=s.variance,l=[];for(let d of Gr(0,r.rank))o.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[Xd(r,u,c,m,p,n),a,i]})}function fee(r,e,t,o,n=.001){return y.arraysEqual(o.slice().sort(),Gr(0,r.rank-1))?pee(r,e,t,o,n):mee(r,e,t,o,n)}var Yd=class extends Me{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ot(e.betaConstraint),this.gammaConstraint=Ot(e.gammaConstraint),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer)}build(e){e=Je(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new At({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training,n=Fe(e),s=n.shape,a=s.length,i=Gr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=mn(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!y.arraysEqual(c,Gr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),w=this.movingVariance.read().reshape(u),_=this.center?this.beta.read().reshape(u):null,k=this.scale?this.gamma.read().reshape(u):null;return Xd(n,b,w,_,k,this.epsilon)}else return Xd(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=fee(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,w,_)=>{V(()=>{let k=1-_,D=b.read(),T=D.sub(w).mul(k);b.write(D.sub(T))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer),betaConstraint:Ft(this.betaConstraint),gammaConstraint:Ft(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Yd.className="BatchNormalization";Q.registerClass(Yd);var Zd=class extends Me{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.betaRegularizer=bt(e.betaRegularizer),this.gammaRegularizer=bt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Je(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==hn(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Fe(e),n=o.shape,s=n.length;return V(()=>{let a=!0,{mean:i,variance:l}=Qc(o,this.axis,a),u=mn(1,s);for(let h of this.axis)u[h]=n[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(n[h]),d.push(1)):(f.push(1),d.push(n[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),Xd(o,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Zd.className="LayerNormalization";Q.registerClass(Zd);function dee(r,e,t){return V(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=to()),t!=="channelsLast"&&t!=="channelsFirst")throw new z(`Unknown data format: ${t}. 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s==="max"?a=ha(r,e,t,i):a=ua(r,e,t,i),n==="channelsFirst"&&(a=Ke(a,[0,3,1,2])),a})}function o3(r,e,t,o,n,s){return V(()=>{$t(n),AC(s),ro(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=to()),s==null&&(s="max"),r=a0(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Qm(r,e,t,i):a=Gm(r,e,t,i),n==="channelsFirst"&&(a=Ke(a,[0,4,1,2,3])),a})}var m0=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ut(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=bo(t,this.poolSize[0],this.padding,this.strides[0]),o=bo(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},th=class extends f0{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),ro(n),Zx(e,t,o,n,s,"max")}};th.className="MaxPooling2D";Q.registerClass(th);var rh=class extends f0{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),ro(n),Zx(e,t,o,n,s,"avg")}};rh.className="AveragePooling2D";Q.registerClass(rh);var d0=class extends Me{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),ro(this.padding),this.inputSpec=[new At({ndim:5})]}computeOutputShape(e){e=Je(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=bo(t,this.poolSize[0],this.padding,this.strides[0]),o=bo(o,this.poolSize[1],this.padding,this.strides[1]),n=bo(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},oh=class extends d0{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),ro(n),o3(e,t,o,n,s,"max")}};oh.className="MaxPooling3D";Q.registerClass(oh);var nh=class extends d0{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),ro(n),o3(e,t,o,n,s,"avg")}};nh.className="AveragePooling3D";Q.registerClass(nh);var h0=class extends Me{constructor(e){super(e);this.inputSpec=[new At({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new 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n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=no(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?gee:e.mergeMode,hee(this.mergeMode),e.weights)throw new Se("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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_=C.expandShapeToKeepDim(w.shape,m);w.shape=_}return u.dtype!=="float32"&&e.disposeData(b.dataId),w}var cP={kernelName:Vo,backendName:"wasm",setupFunc:eJ,kernelFunc:tJ};var pP;function rJ(r){pP=r.wasm.cwrap(Go,null,["number, number, number"])}function nJ(r){let{backend:e,inputs:t,attrs:n}=r,{axis:o,keepDims:s}=n,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=tn(a,o,e);if(f){let w=e.dataIdMap.get(c.dataId).id;w!==i&&(u=c,l=w)}let d=u.shape.length;C.assertAxesAreInnerMostDims("min",p,d);let[h,g]=C.computeOutAndReduceShapes(u.shape,p),y=x.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(x.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(b.dataId).id;pP(l,y,w)}if(f&&e.disposeData(c.dataId),s){let w=C.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var mP={kernelName:Go,backendName:"wasm",setupFunc:rJ,kernelFunc:nJ};var oJ=!1,fP=yt(Wo,oJ);var NC;(function(r){r[r.reflect=0]="reflect",r[r.symmetric=1]="symmetric"})(NC||(NC={}));var dP;function sJ(r){dP=r.wasm.cwrap(jo,null,["number","array","number","number","array","array","number","number"])}function iJ(r){let{inputs:{x:e},backend:t,attrs:{paddings:n,mode:o}}=r,s=n.map((d,h)=>d[0]+e.shape[h]+d[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),l=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new Int32Array(p).buffer);return dP(a,u,e.shape.length,Rt[e.dtype],m,f,NC[o],l),i}var hP={kernelName:jo,backendName:"wasm",kernelFunc:iJ,setupFunc:sJ};var aJ=!0,gP=yt(Uo,aJ);var xP=xt(Gs);function Dp(r,e){let t=new Int32Array(r.wasm.HEAPU8.buffer,e,4),n=t[0],o=t[1],s=t[2],a=t[3];return r.wasm._free(e),{pSelectedIndices:n,selectedSize:o,pSelectedScores:s,pValidOutputs:a}}var yP;function lJ(r){yP=r.wasm.cwrap(Xi,"number",["number","number","number","number","number"])}function uJ(r){let{backend:e,inputs:t,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:a}=n,{boxes:i,scores:l}=t,u=e.dataIdMap.get(i.dataId).id,c=e.dataIdMap.get(l.dataId).id,p=yP(u,c,s,o,a),{pSelectedIndices:m,selectedSize:f,pSelectedScores:d,pValidOutputs:h}=Dp(e,p);return e.wasm._free(d),e.wasm._free(h),e.makeOutput([f],"int32",m)}var bP={kernelName:Xi,backendName:"wasm",setupFunc:lJ,kernelFunc:uJ};var wP;function cJ(r){wP=r.wasm.cwrap(Yi,"number",["number","number","number","number","number","bool"])}function pJ(r){let{backend:e,inputs:t,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:a,padToMaxOutputSize:i}=n,{boxes:l,scores:u}=t,c=e.dataIdMap.get(l.dataId).id,p=e.dataIdMap.get(u.dataId).id,m=wP(c,p,s,o,a,i),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Dp(e,m);e.wasm._free(h);let y=e.makeOutput([d],"int32",f),b=e.makeOutput([],"int32",g);return[y,b]}var _P={kernelName:Yi,backendName:"wasm",setupFunc:cJ,kernelFunc:pJ};var kP;function mJ(r){kP=r.wasm.cwrap(Zi,"number",["number","number","number","number","number","number"])}function fJ(r){let{backend:e,inputs:t,attrs:n}=r,{iouThreshold:o,maxOutputSize:s,scoreThreshold:a,softNmsSigma:i}=n,{boxes:l,scores:u}=t,c=e.dataIdMap.get(l.dataId).id,p=e.dataIdMap.get(u.dataId).id,m=kP(c,p,s,o,a,i),{pSelectedIndices:f,selectedSize:d,pSelectedScores:h,pValidOutputs:g}=Dp(e,m);e.wasm._free(g);let y=e.makeOutput([d],"int32",f),b=e.makeOutput([d],"float32",h);return[y,b]}var vP={kernelName:Zi,backendName:"wasm",setupFunc:mJ,kernelFunc:fJ};var dJ=!1,CP=yt(Ki,dJ,"bool");var IP;function hJ(r){IP=r.wasm.cwrap(Ho,null,["number","number","number","number","number"])}function gJ(r){let{inputs:e,backend:t,attrs:n}=r,{indices:o}=e,{depth:s,onValue:a,offValue:i}=n,l=t.makeOutput([...o.shape,s],"int32"),u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(o.dataId).id;return IP(p,s,a,i,u),l}var NP={kernelName:Ho,backendName:"wasm",setupFunc:hJ,kernelFunc:gJ};function xJ(r){let{inputs:{x:e},backend:t}=r,n=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(n).fill(1),n}var SP={kernelName:Ws,backendName:"wasm",kernelFunc:xJ};function yJ(r){let{inputs:e,backend:t,attrs:n}=r,{axis:o}=n;if(e.length===1)return kx({inputs:{input:e[0]},backend:t,attrs:{dim:o}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{x.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),x.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=kx({inputs:{input:c},backend:t,attrs:{dim:o}});return i.push(p),p}),u=CC({inputs:l,backend:t,attrs:{axis:o}});return i.forEach(c=>t.disposeData(c.dataId)),u}var TP={kernelName:js,backendName:"wasm",kernelFunc:yJ};var AP;function bJ(r){AP=r.wasm.cwrap(qo,null,["number","array","number","number","array","array","number","number"])}function wJ(r){let{inputs:{x:e},backend:t,attrs:{paddings:n,constantValue:o}}=r,s=n.map((d,h)=>d[0]+e.shape[h]+d[1]),a=t.dataIdMap.get(e.dataId).id,i=t.makeOutput(s,e.dtype),l=t.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(e.shape).buffer),c=n.map(d=>d[0]),p=n.map(d=>d[1]),m=new Uint8Array(new Int32Array(c).buffer),f=new Uint8Array(new Int32Array(p).buffer);return AP(a,u,e.shape.length,Rt[e.dtype],m,f,o,l),i}var EP={kernelName:qo,backendName:"wasm",kernelFunc:wJ,setupFunc:bJ};var _J=!1,DP=yt(Ko,_J);var $P;function kJ(r){$P=r.wasm.cwrap(Xo,null,["number","number","number"])}function vJ(r){let{inputs:e,backend:t}=r,{x:n,alpha:o}=e,s=t.dataIdMap.get(n.dataId).id,a=t.dataIdMap.get(o.dataId).id,i=t.makeOutput(n.shape,"float32"),l=t.dataIdMap.get(i.dataId).id;return $P(s,a,l),i}var RP={kernelName:Xo,backendName:"wasm",setupFunc:kJ,kernelFunc:vJ};var FP;function CJ(r){FP=r.wasm.cwrap(Ji,null,["number","number","number","number"])}function IJ(r){let{backend:e,inputs:t,attrs:n}=r,{axis:o,keepDims:s}=n,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=tn(a,o,e),d=p;if(f){let w=e.dataIdMap.get(c.dataId).id;w!==i&&(u=c,l=w,d=C.getInnerMostAxes(d.length,u.shape.length))}C.assertAxesAreInnerMostDims("prod",d,u.shape.length);let[h,g]=C.computeOutAndReduceShapes(u.shape,d),y=x.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(x.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(b.dataId).id;FP(l,y,Rt[b.dtype],w)}if(f&&e.disposeData(c.dataId),s){let w=C.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var OP={kernelName:Ji,backendName:"wasm",setupFunc:CJ,kernelFunc:IJ};var NJ=r=>{let{backend:e,attrs:t}=r,{start:n,stop:o,step:s,dtype:a}=t,i=Au(n,o,s,a),l=e.makeOutput([i.length],a);return e.typedArrayFromHeap(l).set(i),l},PP={kernelName:nl,backendName:"wasm",kernelFunc:NJ};var SJ=!0,MP=yt(Eo,SJ);var LP=xt(Yo);var zP=xt(Jo);var BP;function TJ(r){BP=r.wasm.cwrap(Zo,null,["number","number","number","number","number","number","number","number","number","number"])}function AJ(r){let{backend:e,inputs:t,attrs:n}=r,{images:o}=t,{alignCorners:s,halfPixelCenters:a,size:i}=n,[l,u]=i,[c,p,m,f]=o.shape,d=[c,l,u,f],h=e.dataIdMap.get(o.dataId),g;h.dtype!=="float32"&&(g=Gu({backend:e,inputs:{x:o},attrs:{dtype:"float32"}}),h=e.dataIdMap.get(g.dataId));let y=h.id,b=e.makeOutput(d,"float32");if(x.sizeFromShape(o.shape)===0)return b;let w=e.dataIdMap.get(b.dataId).id;return BP(y,c,p,m,f,l,u,s?1:0,a?1:0,w),g!=null&&e.disposeData(g.dataId),b}var VP={kernelName:Zo,backendName:"wasm",setupFunc:TJ,kernelFunc:AJ};var GP;function EJ(r){GP=r.wasm.cwrap(Qo,null,["number","array","number","array","number","number"])}function DJ(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{dims:s}=n,a=x.parseAxisParam(s,o.shape);if(o.shape.length===0)return Vu({inputs:{x:o},backend:t});let i=t.makeOutput(o.shape,o.dtype),l=t.dataIdMap.get(o.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),p=new Uint8Array(new Int32Array(o.shape).buffer);GP(l,c,a.length,p,o.shape.length,u);let m=Br({inputs:{x:i},attrs:{shape:o.shape},backend:t});return t.disposeData(i.dataId),m}var WP={kernelName:Qo,backendName:"wasm",kernelFunc:DJ,setupFunc:EJ};var jP;function $J(r){jP=r.wasm.cwrap(aa,null,["number","number","number","number","number","number","number","number","array","number","number"])}function RJ(r){let{inputs:e,backend:t,attrs:n}=r,{image:o}=e,{radians:s,fillValue:a,center:i}=n,l=t.makeOutput(o.shape,o.dtype),u=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(l.dataId).id,[p,m,f,d]=o.shape,[h,g]=C.getImageCenter(i,m,f),y=a===0,b=255,w=typeof a=="number"?[a,a,a,y?0:b]:[...a,b],_=new Uint8Array(new Int32Array(w).buffer);return jP(u,p,m,f,d,s,h,g,_,w.length,c),l}var UP={kernelName:aa,backendName:"wasm",kernelFunc:RJ,setupFunc:$J};var HP=xt(es);var qP=xt(ts);var KP;function FJ(r){KP=r.wasm.cwrap(ea,null,["number","number","number","number","number","number","array","number","number"])}function OJ(r){let{backend:e,inputs:t,attrs:n}=r,{indices:o,updates:s}=t,{shape:a}=n,i=e.makeOutput(a,s.dtype);if(x.sizeFromShape(a)===0)return i;let{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:m}=lg.calculateShapes(s,o,a),d=e.dataIdMap.get(o.dataId).id,g=e.dataIdMap.get(s.dataId).id,y=new Uint8Array(new Int32Array(p).buffer),b=e.dataIdMap.get(i.dataId).id;return KP(d,g,Rt[s.dtype],l,u,c,y,m,b),i}var XP={kernelName:ea,backendName:"wasm",setupFunc:FJ,kernelFunc:OJ};var YP;function PJ(r){YP=r.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};wd.className="ThresholdedReLU";J.registerClass(wd);var _d=class extends Le{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new hd().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Re(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};_d.className="Softmax";J.registerClass(_d);function El(r,e,t){if(typeof r=="number")return co(r,e);if(r.length!==e)throw new z(`The ${t} argument must be an integer or tuple of ${e} integers. Received: ${r.length} elements.`);for(let n=0;n<e;++n){let o=r[n];if(!Bz(o))throw new z(`The ${t} argument must be an integer or tuple of ${e} integers. Received: ${JSON.stringify(r)} including a non-integer number ${o}`)}return r}function wn(r,e,t,n,o=1){if(r==null)return r;let s=e+(e-1)*(o-1),a;return t==="same"?a=r:a=r-s+1,Math.floor((a+n-1)/n)}function Ds(r,e,t,n){if(r==null)return null;if(n==="valid")r=r*e+Ss([t-e,0]);else if(n==="same")r=r*e;else throw new z(`Unsupport padding mode: ${n}.`);return r}function kd(r,e){return V(()=>(Ft(e),e==="channelsFirst"?Ue(r,[0,2,3,1]):r))}function _0(r,e){return V(()=>(Ft(e),e==="channelsFirst"?Ue(r,[0,2,3,4,1]):r))}function Vee(r,e,t,n=1,o="valid",s,a=1){return V(()=>{if(s==null&&(s=rn()),Ft(s),r.shape.length!==3)throw new z(`The input of a conv1dWithBias operation should be 3, but is ${r.shape.length} instead.`);if(e.shape.length!==3)throw new z(`The kernel for a conv1dWithBias operation should be 3, but is ${e.shape.length} instead`);if(t!=null&&t.shape.length!==1)throw new z(`The bias for a conv1dWithBias operation should be 1, but is ${e.shape.length} instead`);if(s==="channelsFirst"&&(r=Ue(r,[0,2,1])),o==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=ru(r,e,n,o==="same"?"same":"valid","NWC",a);return t!=null&&(i=on(i,t)),i})}function S3(r,e,t,n=[1,1],o="valid",s,a,i=null){return V(()=>{if(s==null&&(s=rn()),Ft(s),r.rank!==3&&r.rank!==4)throw new z(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new z(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=kd(r,s);if(o==="causal")throw new Se("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=ro.conv2d({x:l,filter:e,strides:n,pad:o==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=Ue(l,[0,3,1,2])),l})}function Gee(r,e,t,n=[1,1,1],o="valid",s,a){return V(()=>{if(s==null&&(s=rn()),Ft(s),r.rank!==4&&r.rank!==5)throw new z(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new z(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=_0(r,s);if(o==="causal")throw new Se("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Km(i,e,n,o==="same"?"same":"valid","NDHWC",a),t!=null&&(i=on(i,t)),s==="channelsFirst"&&(i=Ue(i,[0,4,1,2,3])),i})}var Zp=class extends Le{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Zp.verifyArgs(t),this.rank=e,Ut(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Se(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=El(t.kernelSize,e,"kernelSize"),this.strides=El(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,nn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=Es(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Mt(t.biasConstraint),this.biasRegularizer=wt(t.biasRegularizer),this.activityRegularizer=wt(t.activityRegularizer),this.dilationRate=El(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new z(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new z(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new z(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Gn("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Ax(e.kernelSize,"number",1,3))throw new z(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:As(this.activation),useBias:this.useBias,biasInitializer:vt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Zu=class extends Zp{constructor(e,t){super(e,t);this.kernel=null,Zu.verifyArgs(t),this.filters=t.filters,Ut(this.filters,"filters"),this.kernelInitializer=pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Mt(t.kernelConstraint),this.kernelRegularizer=wt(t.kernelRegularizer)}build(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],o=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",o,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return V(()=>{e=Re(e);let n,o=this.bias==null?null:this.bias.read(),s=Ex(this.activation.getClassName());if(s!=null&&this.rank===2)n=S3(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)n=Vee(e,this.kernel.read(),o,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=S3(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Gee(e,this.kernel.read(),o,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Se("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=Xe(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<n.length;++s){let a=wn(n[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let o=[e[0]];return this.dataFormat==="channelsLast"?(o=o.concat(t),o.push(this.filters)):(o.push(this.filters),o=o.concat(t)),o}getConfig(){let e={filters:this.filters,kernelInitializer:vt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Pt(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new z(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Dl=class extends Zu{constructor(e){super(2,e);Dl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ax(e.kernelSize,"number",1,2))throw new z(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Dl.className="Conv2D";J.registerClass(Dl);var $l=class extends Zu{constructor(e){super(3,e);$l.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new z(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};$l.className="Conv3D";J.registerClass($l);var vd=class extends Dl{constructor(e){super(e);if(this.inputSpec=[new Ct({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Xe(e),e.length!==4)throw new z("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ct({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Re(e);if(n.shape.length!==4)throw new z(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=o[a],u=o[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=Ds(l,m,c,this.padding),h=Ds(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,1]));let y=nu(n,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=Ue(y,[0,3,1,2])),this.bias!=null&&(y=on(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=Xe(e);let t=e.slice(),n,o,s;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3):(n=3,o=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[o]=Ds(t[o],l,a,this.padding),t[s]=Ds(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};vd.className="Conv2DTranspose";J.registerClass(vd);var Cd=class extends $l{constructor(e){super(e);if(this.inputSpec=[new Ct({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new z(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Xe(e),e.length!==5)throw new z("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new z("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],o=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",o,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ct({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Re(e);if(n.shape.length!==5)throw new z(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let o=n.shape,s=o[0],a,i,l;this.dataFormat==="channelsFirst"?(l=2,a=3,i=4):(l=1,a=2,i=3);let u=o[l],c=o[a],p=o[i],m=this.kernelSize[0],f=this.kernelSize[1],d=this.kernelSize[2],h=this.strides[0],g=this.strides[1],y=this.strides[2],b=Ds(u,h,m,this.padding),w=Ds(c,g,f,this.padding),_=Ds(p,y,d,this.padding),I=[s,b,w,_,this.filters];this.dataFormat!=="channelsLast"&&(n=Ue(n,[0,2,3,4,1]));let E=qw(n,this.kernel.read(),I,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(E=Ue(E,[0,4,1,2,3])),this.bias!==null&&(E=on(E,this.bias.read(),this.dataFormat)),this.activation!==null&&(E=this.activation.apply(E)),E})}computeOutputShape(e){e=Xe(e);let t=e.slice(),n,o,s,a;this.dataFormat==="channelsFirst"?(n=1,o=2,s=3,a=4):(n=4,o=1,s=2,a=3);let i=this.kernelSize[0],l=this.kernelSize[1],u=this.kernelSize[2],c=this.strides[0],p=this.strides[1],m=this.strides[2];return t[n]=this.filters,t[o]=Ds(t[o],c,i,this.padding),t[s]=Ds(t[s],p,l,this.padding),t[a]=Ds(t[a],m,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Cd.className="Conv3DTranspose";J.registerClass(Cd);var k0=class extends Zu{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new z("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new z("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new z(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=wt(t.depthwiseRegularizer),this.depthwiseConstraint=Mt(t.depthwiseConstraint),this.pointwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=wt(t.pointwiseRegularizer),this.pointwiseConstraint=Mt(t.pointwiseConstraint)}build(e){if(e=Xe(e),e.length<this.rank+2)throw new z(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],o=this.kernelSize.concat([n,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",o,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Ct({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{e=Re(e);let n;if(this.rank===1)throw new Se("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ue(e,[0,2,3,1])),n=cf(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=on(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ue(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.pointwiseInitializer=vt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseConstraint),e.pointwiseConstraint=Pt(this.pointwiseConstraint),e}};k0.className="SeparableConv";var Id=class extends k0{constructor(e){super(2,e)}};Id.className="SeparableConv2D";J.registerClass(Id);var Ju=class extends Zu{constructor(e){super(1,e);Ju.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Ax(e.kernelSize,"number",1,1))throw new z(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Ju.className="Conv1D";J.registerClass(Ju);var Nd=class extends Le{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Re(e),this.dataFormat==="channelsLast"){let n=Jf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Jf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Jf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Jf(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Nd.className="Cropping2D";J.registerClass(Nd);var Sd=class extends Le{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,Mz(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Re(e),o=n.shape;if(this.dataFormat==="channelsFirst"){n=Ue(n,[0,2,3,1]);let s=this.size[0]*o[2],a=this.size[1]*o[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a]);return Ue(i,[0,3,1,2])}else{let s=this.size[0]*o[1],a=this.size[1]*o[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([s,a]):n.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Sd.className="UpSampling2D";J.registerClass(Sd);function Wee(r,e,t=[1,1],n="valid",o,s){return V(()=>{o==null&&(o=rn()),Ft(o);let a=kd(r,o);if(r.rank!==4)throw new z(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new z(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=hs(a,e,t,n==="same"?"same":"valid","NHWC",s),o==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}var Td=class extends Zp{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Mt(e.depthwiseConstraint),this.depthwiseRegularizer=wt(e.depthwiseRegularizer)}build(e){if(e=Xe(e),e.length<4)throw new z(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new z(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],o=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",o,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Re(e);let n=Wee(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=on(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=wn(t,this.kernelSize[0],this.padding,this.strides[0]),a=wn(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],o,s,a]:[e[0],s,a,o]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=vt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Pt(this.depthwiseRegularizer),e}};Td.className="DepthwiseConv2D";J.registerClass(Td);function v0(r,e,t,n){if(Array.isArray(r)){if(e!=null||t!=null)throw new z("When inputs is an array, neither initialState or constants should be provided");n!=null&&(t=r.slice(r.length-n,r.length),r=r.slice(0,r.length-n)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function o(s){return s==null||Array.isArray(s)?s:[s]}return e=o(e),t=o(t),{inputs:r,initialState:e,constants:t}}function C0(r,e,t,n=!1,o,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new z(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Gr(2,l));if(e=Ue(e,u),s!=null)throw new Se("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),o!=null&&(o=o.asType("bool").asType("float32"),o.rank===l-1&&(o=ur(o,-1)),o=Ue(o,u)),n&&(e=qt(e,0),o!=null&&(o=qt(o,0)));let c=[],p,m=t,f=e.shape[0],d=fr(e),h;o!=null&&(h=fr(o));for(let y=0;y<f;++y){let b=d[y],w=V(()=>r(b,m));if(o==null)p=w[0],m=w[1];else{let _=V(()=>{let I=h[y],E=or(I).sub(I),$=w[0].mul(I).add(m[0].mul(E)),D=m.map((O,M)=>w[1][M].mul(I).add(O.mul(E)));return{output:$,newStates:D}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=Bt(c,1)),[p,g,m]})}var Dn=class extends Le{constructor(e){super(e);let t;if(e.cell==null)throw new z("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Jp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new z("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Ct({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Gr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Lx(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],o;if(this.returnSequences?o=[e[0],e[1],n]:o=[e[0],n],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[o].concat(s)}else return o}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let o=this.states.map(s=>null);return[n].concat(o)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Se("Constants support is not implemented in RNN yet.");Lx(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,o=e.slice(2);this.inputSpec[0]=new Ct({shape:[n,null,...o]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Se("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!x.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new z(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Ct({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new En("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ht([n,o])):this.states_=[ht([n,this.cell.stateSize])];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(o=>ht([n,o])):this.states_[0]=ht([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let o=0;o<this.states_.length;++o){let s=e[o],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[o]:this.cell.stateSize,i=[n,a];if(!x.arraysEqual(s.shape,i))throw new z(`State ${o} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[o]=s}}this.states_=this.states_.map(o=>Et(o.clone()))})}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=v0(e,n,o,this.numConstants);e=s.inputs,n=s.initialState,o=s.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Ct({shape:u.shape}));i=i.concat(this.stateSpec)}if(o!=null&&(t.constants=o,a=a.concat(o),this.numConstants=o.length),a[0]instanceof sn){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;e=Re(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new z(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:o},u=C0((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,n,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,o);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=ht(e.shape);return t=ge(t,[1,2]),t=Pa(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Rx(t,[1,n]):t):this.cell.stateSize>1?[Rx(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Dn.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let o=t.cell,s=an(o,n);return new e(Object.assign(t,{cell:s}))}};Dn.className="RNN";J.registerClass(Dn);var Rl=class extends Le{},Qp=class extends Rl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Es(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Mt(e.kernelConstraint),this.recurrentConstraint=Mt(e.recurrentConstraint),this.biasConstraint=Mt(e.biasConstraint),this.dropout=ju([1,Ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ju([1,Ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Xe(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new z(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let o=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=za({ones:()=>or(e),rate:this.dropout,training:o})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=za({ones:()=>or(n),rate:this.recurrentDropout,training:o}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=go(P(e,a),this.kernel.read()):s=go(e,this.kernel.read()),this.bias!=null&&(s=on(s,this.bias.read())),i!=null&&(n=P(n,i));let l=ee(s,go(n,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:As(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Qp.className="SimpleRNNCell";J.registerClass(Qp);var Ad=class extends Dn{constructor(e){e.cell=new Qp(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return new e(t)}};Ad.className="SimpleRNN";J.registerClass(Ad);var em=class extends Rl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new z("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Ut(this.units,"units"),this.activation=Es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Mt(e.kernelConstraint),this.recurrentConstraint=Mt(e.recurrentConstraint),this.biasConstraint=Mt(e.biasConstraint),this.dropout=ju([1,Ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ju([1,Ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Xe(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{if(e=e,e.length!==2)throw new z(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,o=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=za({ones:()=>or(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=za({ones:()=>or(o),rate:this.recurrentDropout,training:n,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=P(e,s[0]));let c=go(e,this.kernel.read());this.useBias&&(c=on(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(o=P(o,a[0]));let p=this.recurrentKernel.read(),[m,f]=mr(p,[2*this.units,this.units],p.rank-1),d=go(o,m),[h,g,y]=mr(c,3,c.rank-1),[b,w]=mr(d,2,d.rank-1);i=this.recurrentActivation.apply(ee(h,b)),l=this.recurrentActivation.apply(ee(g,w));let _=go(P(l,o),f);u=this.activation.apply(ee(y,_));let I=ee(P(i,o),P(ee(1,qe(i)),u));return[I,I]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:As(this.activation),recurrentActivation:As(this.recurrentActivation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};em.className="GRUCell";J.registerClass(em);var Ed=class extends Dn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new em(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Ed.className="GRU";J.registerClass(Ed);var Fl=class extends Rl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Ut(this.units,"units"),this.activation=Es(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Es(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=wt(e.kernelRegularizer),this.recurrentRegularizer=wt(e.recurrentRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.kernelConstraint=Mt(e.kernelConstraint),this.recurrentConstraint=Mt(e.recurrentConstraint),this.biasConstraint=Mt(e.biasConstraint),this.dropout=ju([1,Ss([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=ju([1,Ss([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Xe(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let o;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;o=new(t=class extends xn{apply(l,u){let c=s.apply([a]),p=new Hu().apply([a]),m=s.apply([a*2]);return UC(UC(c,p),m)}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,o,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new z(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=za({ones:()=>or(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=za({ones:()=>or(o),rate:this.recurrentDropout,training:n,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=P(e,a[0]));let m=go(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(o=P(o,i[0])),m=ee(m,go(o,this.recurrentKernel.read())),this.useBias&&(m=on(m,this.bias.read()));let[f,d,h,g]=mr(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=ee(P(u,s),P(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let y=P(p,this.activation.apply(c));return[y,y,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:As(this.activation),recurrentActivation:As(this.recurrentActivation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),recurrentInitializer:vt(this.recurrentInitializer),biasInitializer:vt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),recurrentConstraint:Pt(this.recurrentConstraint),biasConstraint:Pt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};Fl.className="LSTMCell";J.registerClass(Fl);var Dd=class extends Dn{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Fl(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Dd.className="LSTM";J.registerClass(Dd);var Jp=class extends Rl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return V(()=>{e=e;let n=e.slice(1),o=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?o.push(n.splice(0,i.stateSize.length)):o.push(n.splice(0,1));o.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];n=o[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=l.call(a,t),s.push(a.slice(1))}n=[];for(let i of s.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){Lx(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,o)=>{Ns(`RNNCell_${o}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),o={cells:this.cells.map(t)};return Object.assign({},e,o)}static fromConfig(e,t,n={}){let o=[];for(let s of t.cells)o.push(an(s,n));return new e({cells:o})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return id(e)}setWeights(e){let t=[];for(let n of this.cells){let o=n.weights.length,s=e.splice(o);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],s[a]])}Hp(t)}};Jp.className="StackedRNNCells";J.registerClass(Jp);function za(r){let{ones:e,rate:t,training:n=!1,count:o=1}=r,s=()=>Ox(e(),t),a=()=>Cl(s,e,n);return!o||o<=1?Et(a().clone()):Array(o).fill(void 0).map(a).map(l=>Et(l.clone()))}var jee=function(r,e){var t={};for(var n in r)Object.prototype.hasOwnProperty.call(r,n)&&e.indexOf(n)<0&&(t[n]=r[n]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var o=0,n=Object.getOwnPropertySymbols(r);o<n.length;o++)e.indexOf(n[o])<0&&Object.prototype.propertyIsEnumerable.call(r,n[o])&&(t[n[o]]=r[n[o]]);return t};var I0=class extends Dn{constructor(e){if(e.unroll)throw new Se("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Se("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ct({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Ae(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ae(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new z("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,o=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:n,training:o,initialState:s})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)],a=ht(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new En("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,o=this.computeSingleOutputShape(n),s=[o[0],...o.slice(2)];if(n[0]==null)throw new z("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ht(s)):this.states_=[ht(s)];else if(e==null)Ae(this.states_),this.keptStates!=null&&(Ae(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ht(s)):this.states_[0]=ht(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new z(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Ae(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!x.arraysEqual(l.shape,u))throw new z(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Et(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:o,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=wn(u,o[0],s,a[0],i[0]),m=wn(c,o[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[n,p,m]:[p,m,n]]}};I0.className="ConvRNN2D";var tm=class extends Fl{constructor(e){let{filters:t,kernelSize:n,strides:o,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Ut(this.filters,"filters"),this.kernelSize=El(n,2,"kernelSize"),this.kernelSize.forEach(l=>Ut(l,"kernelSize")),this.strides=El(o||1,2,"strides"),this.strides.forEach(l=>Ut(l,"strides")),this.padding=s||"valid",nn(this.padding),this.dataFormat=a||"channelsLast",Ft(this.dataFormat),this.dilationRate=El(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Ut(l,"dilationRate"))}build(e){var t;e=Xe(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new z(`The channel dimension of the input should be defined. Found ${e[n]}`);let o=e[n],s=4,a=this.kernelSize.concat([o,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends xn{apply(m,f){let d=u.apply([c]),h=Qt([c]),g=u.apply([c*2]);return Pp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new z(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,o=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=za({ones:()=>or(o),rate:this.dropout,training:n,count:i}));let l=this.dropoutMask,u=(Q,ie,ce)=>!ie||!ie[ce]?Q:P(ie[ce],Q),c=u(o,l,0),p=u(o,l,1),m=u(o,l,2),f=u(o,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=za({ones:()=>or(s),rate:this.recurrentDropout,training:n,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),y=u(s,d,2),b=u(s,d,3),w=3,[_,I,E,$]=mr(this.kernel.read(),i,w),[D,O,M,G]=this.useBias?mr(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,D,this.padding),p=this.inputConv(p,I,O,this.padding),m=this.inputConv(m,E,M,this.padding),f=this.inputConv(f,$,G,this.padding);let[j,U,H,q]=mr(this.recurrentKernel.read(),i,w);h=this.recurrentConv(h,j),g=this.recurrentConv(g,U),y=this.recurrentConv(y,H),b=this.recurrentConv(b,q);let X=this.recurrentActivation.apply(ee(c,h)),ne=this.recurrentActivation.apply(ee(p,g)),Y=ee(P(ne,a),P(X,this.activation.apply(ee(m,y)))),re=P(this.recurrentActivation.apply(ee(f,b)),this.activation.apply(Y));return[re,re,Y]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=jee(e,["units"]),o={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,o)}inputConv(e,t,n,o){let s=Jr(e,t,this.strides,o||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?on(s,n,this.dataFormat):s}recurrentConv(e,t){return Jr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};tm.className="ConvLSTM2DCell";J.registerClass(tm);var $d=class extends I0{constructor(e){let t=new tm(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};$d.className="ConvLSTM2D";J.registerClass($d);var rm=class extends Le{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let o=0;o<this.noiseShape.length;++o)n.push(this.noiseShape[o]==null?t[o]:this.noiseShape[o]);return n}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e);if(0<this.rate&&this.rate<1){let o=t.training==null?!1:t.training,s=this.getNoiseShape(n);return Cl(()=>Ox(n,this.rate,s,this.seed),()=>n,o)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};rm.className="Dropout";J.registerClass(rm);var Rd=class extends rm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Rd.className="SpatialDropout1D";J.registerClass(Rd);var Fd=class extends Le{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Ut(this.units,"units"),this.activation=Es(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Mt(e.kernelConstraint),this.biasConstraint=Mt(e.biasConstraint),this.kernelRegularizer=wt(e.kernelRegularizer),this.biasRegularizer=wt(e.biasRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Xe(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=Xe(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e),o=Ex(this.activation.getClassName()),s;return o!=null?s=go(n,this.kernel.read(),o,this.bias?this.bias.read():null):(s=go(n,this.kernel.read()),this.bias!=null&&(s=on(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:As(this.activation),useBias:this.useBias,kernelInitializer:vt(this.kernelInitializer),biasInitializer:vt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Pt(this.kernelConstraint),biasConstraint:Pt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Fd.className="Dense";J.registerClass(Fd);var Od=class extends Le{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Xe(e);for(let t of e.slice(1))if(t==null)throw new z(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],ho(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let o=[0];for(let s=2;s<n.rank;++s)o.push(s);o.push(1),n=n.transpose(o)}return jz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};Od.className="Flatten";J.registerClass(Od);var Pd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.activation=Es(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.activation.apply(n)})}getConfig(){let e={activation:As(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Pd.className="Activation";J.registerClass(Pd);var Md=class extends Le{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return V(()=>(e=Re(e),Gz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};Md.className="RepeatVector";J.registerClass(Md);var Ld=class extends Le{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",o=t.slice(),s=1,a=null;for(let l=0;l<o.length;++l){let u=o[l];if(this.isUnknown(u))if(a===null)a=l;else throw new z("Can only specifiy one unknown dimension.");else s*=u}let i=ho(e);if(a!==null){if(s===0||i%s!=0)throw new z(n);o[a]=i/s}else if(i!==s)throw new z(n);return o}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e),o=n.shape,s=o.slice(0,1).concat(this.fixUnknownDimension(o.slice(1),this.targetShape));return n.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Ld.className="Reshape";J.registerClass(Ld);var zd=class extends Le{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Gr(1,e.dims.length+1);if(!x.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Ct({ndim:this.dims.length+1})]}computeOutputShape(e){e=Xe(e);let t=e.slice();return this.dims.forEach((n,o)=>{t[o+1]=e[n]}),t}call(e,t){return Ue(Re(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};zd.className="Permute";J.registerClass(zd);var Bd=class extends Le{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Re(e),o=-1;return ml(to(n,this.maskValue),o)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e),o=-1,s=!0,a=ml(to(n,this.maskValue),o,s);return n.mul(a.asType(n.dtype))})}};Bd.className="Masking";J.registerClass(Bd);var Vd=class extends Le{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(bt(e.inputLength))}this.inputDim=e.inputDim,Ut(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Ut(this.outputDim,"outputDim"),this.embeddingsInitializer=pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=wt(e.embeddingsRegularizer),this.activityRegularizer=wt(e.activityRegularizer),this.embeddingsConstraint=Mt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return V(()=>this.maskZero?(e=Re(e),to(e,Ie(e))):null)}computeOutputShape(e){if(e=Xe(e),this.inputLength==null)return[...e,this.outputDim];let t=bt(this.inputLength);if(t.length!==e.length-1)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let o=0;o<t.length;++o){let s=t[o],a=e[o+1];if(s!=null&&a!=null&&s!==a)throw new z(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e);return n.dtype!=="int32"&&(n=Oa(n,"int32")),Fx(this.embeddings.read(),n.as1D()).reshape(Xe(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:vt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Pt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Vd.className="Embedding";J.registerClass(Vd);var Ol=class extends Le{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Se}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let o=0;o<t.length;++o){let s=e[e.length-t.length+o],a=t[o];if(s==null||a==null||s<0||a<0)n.push(null);else if(s===1)n.push(a);else if(a===1)n.push(s);else{if(s!==a)throw new z("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(s)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Xe(e)]),e=e,e.length<2)throw new z(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=fo(t),t.length>1)throw new z(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let o=e.map(s=>s.length);e.indexOf(null)===-1&&fo(o).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],o=e.map(s=>s.rank);if(o.indexOf(null)===-1){let s=Ss(o);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=Pa(a,1);n.push(a)}return this.mergeFunction(n)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(ho(c.slice(1))));f=Ue(f,[1,0]),f=f.reshape(m),n.push(f),s=!0}else if(u>1){let c=Gr(1,u).concat([0]);n.push(Ue(l,c)),s=!0}else n.push(l)}let a=this.mergeFunction(n),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=Ue(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Gr(0,i-1));a=Ue(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let o=1;o<e.length;++o){let s=e[o]==null?null:e[o].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let n=[];for(let o of e)o!=null&&o[0]!==null&&n.push(o[0]);return n=fo(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an Array");if(!Array.isArray(e))throw new z("`inputs` should be an Array");if(t.length!==e.length)throw new z(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(o=>o==null))return null;t=t.map(o=>o==null?o:ur(o,0));let n=t[0];for(let o=1;o<t.length-1;++o)n=xr(n,t[o]);return n})}},Gd=class extends Ol{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ee(t,e[n]);return t})}};Gd.className="Add";J.registerClass(Gd);var Wd=class extends Ol{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=P(t,e[n]);return t})}};Wd.className="Multiply";J.registerClass(Wd);var jd=class extends Ol{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ee(t,e[n]);return P(1/e.length,t)})}};jd.className="Average";J.registerClass(jd);var Ud=class extends Ol{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=en(t,e[n]);return t})}};Ud.className="Maximum";J.registerClass(Ud);var Hd=class extends Ol{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bs(t,e[n]);return t})}};Hd.className="Minimum";J.registerClass(Hd);var qd=class extends Ol{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new z("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let o of e)if(o!=null){t=!1;break}if(t)return;let n=[];for(let o=0;o<e.length;++o){let s=e[o].slice();s.splice(this.axis,1);let a=!1;for(let i of n)if(x.arraysEqual(i,s)){a=!0;break}a||n.push(s)}if(n.length>1)throw new z("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>Pp(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new z("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),o=this.axis<0?n.length+this.axis:this.axis;for(let s of t.slice(1)){if(n[o]==null||s[o]==null){n[o]=null;break}n[o]+=s[o]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new z("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new z("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new z(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let o=[];for(let a=0;a<e.length;++a)t[a]==null?o.push(or(e[a]).asType("bool")):t[a].rank<e[a].rank?o.push(ur(t[a],-1)):o.push(t[a]);let s=Qe(o,this.axis);return Ql(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};qd.className="Concatenate";J.registerClass(qd);function Kd(r,e){for(;r<0;)r+=e;return r}function Uee(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Se("batchDot is not implemented for tensors of 4D or higher rank yet");if(x.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),x.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Se("batchDot is not implemented for complex64-type Tensors yet.");let n=r.shape.length,o=e.shape.length;t==null&&(t=[n-1,o-2]);let s=t;return V(()=>{let a;if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;n>o?l=n+o-3:l=n-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var Xd=class extends Ol{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){x.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);if(t[o[0]]!==n[o[1]])throw new z(`Dimension incompatibility: ${t[o[0]]} !== ${n[o[1]]}`)}mergeFunction(e){if(e.length!==2)throw new z(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],o;return Array.isArray(this.axes)?o=this.axes.map((s,a)=>Kd(s,e[a].shape.length)):o=[Kd(this.axes,t.shape.length),Kd(this.axes,n.shape.length)],this.normalize&&(t=ad(t,o[0]),n=ad(n,o[1])),Uee(t,n,o)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Kd(this.axes,e.length),Kd(this.axes,t.length)],n}computeOutputShape(e){x.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Se("Dot layer does not support tensors of 4D or higher rank yet.");let o=this.interpretAxes(t,n);t.splice(o[0],1),n.splice(o[1],1),n.splice(0,1);let s=t.concat(n);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Xd.className="Dot";J.registerClass(Xd);var Yd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e);return Cl(()=>Mp(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};Yd.className="GaussianNoise";J.registerClass(Yd);var Zd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Re(e);return this.rate>0&&this.rate<1?Cl(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return n.mul(Mp(n.shape,1,s))},()=>n,t.training||!1):n})}};Zd.className="GaussianDropout";J.registerClass(Zd);var Jd=class extends Le{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Re(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return V(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Cl(()=>{let s=Re(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=dn(ws(n),this.rate);u=Oa(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Re(e),t.training||!1)}return e})}};Jd.className="AlphaDropout";J.registerClass(Jd);function Qd(r,e,t,n,o,s=.001){let a;if(r.rank===2)a=zw(r,e,t,n,o,s);else if(r.rank===3)a=Bw(r,e,t,n,o,s);else if(r.rank===4)a=Vw(r,e,t,n,o,s);else throw new Se(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function Hee(r,e,t,n,o=.001){return V(()=>{let s=rp(r,n),a=s.mean,i=s.variance;return[Qd(r,a,i,t,e,o),a,i]})}function qee(r,e,t,n,o=.001){return V(()=>{let s=rp(r,n),a=s.mean,i=s.variance,l=[];for(let d of Gr(0,r.rank))n.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[Qd(r,u,c,m,p,o),a,i]})}function Kee(r,e,t,n,o=.001){return x.arraysEqual(n.slice().sort(),Gr(0,r.rank-1))?Hee(r,e,t,n,o):qee(r,e,t,n,o)}var eh=class extends Le{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Mt(e.betaConstraint),this.gammaConstraint=Mt(e.gammaConstraint),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer)}build(e){e=Xe(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new z(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ct({ndim:e.length,axes:{[t]:n}})];let o=[n];this.scale&&(this.gamma=this.addWeight("gamma",o,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",o,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",o,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",o,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,o=Re(e),s=o.shape,a=s.length,i=Gr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=co(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!x.arraysEqual(c,Gr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),w=this.movingVariance.read().reshape(u),_=this.center?this.beta.read().reshape(u):null,I=this.scale?this.gamma.read().reshape(u):null;return Qd(o,b,w,_,I,this.epsilon)}else return Qd(o,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return m();let[f,d,h]=Kee(o,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,w,_)=>{V(()=>{let I=1-_,E=b.read(),$=E.sub(w).mul(I);b.write(E.sub($))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),movingMeanInitializer:vt(this.movingMeanInitializer),movingVarianceInitializer:vt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Pt(this.betaConstraint),gammaConstraint:Pt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};eh.className="BatchNormalization";J.registerClass(eh);var th=class extends Le{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.betaRegularizer=wt(e.betaRegularizer),this.gammaRegularizer=wt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Xe(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==fo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(s=>e[s]),o=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,o):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,o):this.beta=null,this.built=!0}call(e,t){let n=Re(e),o=n.shape,s=o.length;return V(()=>{let a=!0,{mean:i,variance:l}=rp(n,this.axis,a),u=co(1,s);for(let h of this.axis)u[h]=o[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(o[h]),d.push(1)):(f.push(1),d.push(o[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),Qd(n,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:vt(this.betaInitializer),gammaInitializer:vt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};th.className="LayerNormalization";J.registerClass(th);function Xee(r,e,t){return V(()=>{if(r.rank!==4)throw new z(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new z("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=rn()),t!=="channelsLast"&&t!=="channelsFirst")throw new z(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return t==="channelsFirst"?n=[[0,0],[0,0],e[0],e[1]]:n=[[0,0],e[0],e[1],[0,0]],Mr(r,n)})}var rh=class extends Le{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?rn():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new z(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new z(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new z(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ct({ndim:4})]}computeOutputShape(e){e=Xe(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>Xee(Re(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};rh.className="ZeroPadding2D";J.registerClass(rh);function ty(r,e,t,n,o,s){return V(()=>{Ft(o),WC(s),nn(n),t==null&&(t=[1,1]),n==null&&(n="valid"),o==null&&(o=rn()),s==null&&(s="max"),r=kd(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=wa(r,e,t,i):a=fa(r,e,t,i),o==="channelsFirst"&&(a=Ue(a,[0,3,1,2])),a})}function T3(r,e,t,n,o,s){return V(()=>{Ft(o),WC(s),nn(n),t==null&&(t=[1,1,1]),n==null&&(n="valid"),o==null&&(o=rn()),s==null&&(s="max"),r=_0(r,o);let a,i=n==="same"?"same":"valid";return s==="max"?a=of(r,e,t,i):a=Hm(r,e,t,i),o==="channelsFirst"&&(a=Ue(a,[0,4,1,2,3])),a})}var N0=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new z(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Ut(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new z(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,nn(this.padding),this.inputSpec=[new Ct({ndim:3})]}computeOutputShape(e){e=Xe(e);let t=wn(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=Pa(Re(e),2);let n=this.poolingFunction(Re(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Sn(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},nh=class extends N0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),nn(o),ty(e,t,n,o,s,"max")}};nh.className="MaxPooling1D";J.registerClass(nh);var oh=class extends N0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),nn(o),ty(e,t,n,o,s,"avg")}};oh.className="AveragePooling1D";J.registerClass(oh);var S0=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new z(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),nn(this.padding),this.inputSpec=[new Ct({ndim:4})]}computeOutputShape(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=wn(t,this.poolSize[0],this.padding,this.strides[0]),n=wn(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Re(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},sh=class extends S0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),nn(o),ty(e,t,n,o,s,"max")}};sh.className="MaxPooling2D";J.registerClass(sh);var ih=class extends S0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),nn(o),ty(e,t,n,o,s,"avg")}};ih.className="AveragePooling2D";J.registerClass(ih);var T0=class extends Le{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new z(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Ut(this.poolSize,"poolSize"),Ut(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),nn(this.padding),this.inputSpec=[new Ct({ndim:5})]}computeOutputShape(e){e=Xe(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],o=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=wn(t,this.poolSize[0],this.padding,this.strides[0]),n=wn(n,this.poolSize[1],this.padding,this.strides[1]),o=wn(o,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,o]:[e[0],t,n,o,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Re(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ah=class extends T0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),nn(o),T3(e,t,n,o,s,"max")}};ah.className="MaxPooling3D";J.registerClass(ah);var lh=class extends T0{constructor(e){super(e)}poolingFunction(e,t,n,o,s){return Ft(s),nn(o),T3(e,t,n,o,s,"avg")}};lh.className="AveragePooling3D";J.registerClass(lh);var A0=class extends Le{constructor(e){super(e);this.inputSpec=[new Ct({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Se}},uh=class extends A0{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Re(e);return dt(n,1)})}};uh.className="GlobalAveragePooling1D";J.registerClass(uh);var ch=class extends A0{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Re(e);return pr(n,1)})}};ch.className="GlobalMaxPooling1D";J.registerClass(ch);var E0=class extends Le{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.inputSpec=[new Ct({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Se}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},ph=class extends E0{call(e,t){return V(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?dt(n,[1,2]):dt(n,[2,3])})}};ph.className="GlobalAveragePooling2D";J.registerClass(ph);var mh=class extends E0{call(e,t){return V(()=>{let n=Re(e);return this.dataFormat==="channelsLast"?pr(n,[1,2]):pr(n,[2,3])})}};mh.className="GlobalMaxPooling2D";J.registerClass(mh);var D0=class extends Le{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let o=t.layer,s=an(o,n);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},fh=class extends D0{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Xe(e),e.length<3)throw new z(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=Xe(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),o=e[1];return[n[0],o].concat(n.slice(1))}call(e,t){return V(()=>(e=Re(e),C0((a,i)=>[Re(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};fh.className="TimeDistributed";J.registerClass(fh);function Yee(r){pi(Pz,"BidirectionalMergeMode",r)}var Zee="concat",dh=class extends D0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=an(n),t.goBackwards=t.goBackwards!==!0;let o={};if(o.className=e.layer.getClassName(),o.config=t,this.backwardLayer=an(o),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?Zee:e.mergeMode,Yee(this.mergeMode),e.weights)throw new Se("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,o,s;return this.returnState&&(s=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,o=[n]):this.mergeMode==null?o=[n,n.slice()]:o=[n],this.returnState?this.mergeMode==null?o.concat(s).concat(s.slice()):[n].concat(s).concat(s.slice()):br(o)}apply(e,t){let n=t==null?null:t.initialState,o=t==null?null:t.constants;t==null&&(t={});let s=v0(e,n,o,this.numConstants);if(e=s.inputs,n=s.initialState,o=s.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&o==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new z("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(p=>new Ct({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(o!=null)throw new Se("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof sn;for(let u of a)if(u instanceof sn!==l)throw new z("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t.initialState,o,s;if(n==null)o=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=n.slice(0,n.length/2),u=n.slice(n.length/2);o=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(o)&&(a=o.slice(1).concat(s.slice(1))),o=o[0],s=s[0]),this.returnSequences&&(s=qt(s,1));let i;return this.mergeMode==="concat"?i=Pp([o,s]):this.mergeMode==="sum"?i=ee(o,s):this.mergeMode==="ave"?i=P(.5,ee(o,s)):this.mergeMode==="mul"?i=P(o,s):this.mergeMode==null&&(i=[o,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ns(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ns(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(s).concat(s):[n].concat(s).concat(s)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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o=C("tensorListId",r,e,t),n=C("elementShape",r,e,t),s=C("elementDType",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case"TensorListSplit":{let o=C("tensor",r,e,t),n=C("elementShape",r,e,t),s=C("lengths",r,e,t),a=C3(o,s,n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function N3(r,e,t){let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=o==="fusedbatchnorm",l=C("numArgs",r,e,t);if(s){if(a&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(i)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let u=C("strides",r,e,t),c=ph(r,e,t),p=C("dataFormat",r,e,t).toUpperCase(),m=C("dilations",r,e,t),[f,d]=C("args",r,e,t),h=C("leakyreluAlpha",r,e,t);return{stride:u,pad:c,dataFormat:p,dilations:m,biasArg:f,preluArg:d,activationFunc:n,leakyreluAlpha:h}}var S3=(r,e,t)=>{switch(r.op){case"Conv1D":{let o=C("stride",r,e,t),n=C("pad",r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilation",r,e,t);return[Yl(C("x",r,e,t),C("filter",r,e,t),o,n,s,a)]}case"Conv2D":{let o=C("strides",r,e,t),n=ph(r,e,t),s=C("dataFormat",r,e,t).toUpperCase(),a=C("dilations",r,e,t);return[Jr(C("x",r,e,t),C("filter",r,e,t),[o[1],o[2]],n,s,[a[1],a[2]])]}case"_FusedConv2D":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=N3(r,e,t);return[rn.conv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:o,pad:n,dataFormat:s,dilations:a,biasArg:i,preluArg:l,activationFunc:u,leakyreluAlpha:c}=N3(r,e,t);return[rn.depthwiseConv2d({x:C("x",r,e,t),filter:C("filter",r,e,t),strides:[o[1],o[2]],pad:n,dataFormat:s,dilations:[a[1],a[2]],bias:i,activation:u,preluActivationWeights:l,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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O3=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[je(C("a",r,e,t),C("b",r,e,t),C("transposeA",r,e,t),C("transposeB",r,e,t))];case"Transpose":return[Ke(C("x",r,e,t),C("perm",r,e,t))];case"_FusedMatMul":let[o,n]=C("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=C("numArgs",r,e,t),l=C("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=C("args",r,e,t);return[rn.matMul({a:C("a",r,e,t),b:C("b",r,e,t),transposeA:C("transposeA",r,e,t),transposeB:C("transposeB",r,e,t),bias:u,activation:n,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function K0(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>so(m)[0]),c=[];o!=null&&(c=o.map(m=>so(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((q0(m)||mre(m)||fre(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function 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Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new tm(e.functions[o],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(o=>e[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return 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Kt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},hB=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Ae(e.value)}return this.upstream.next()}},gB=class extends Kt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},xB=class extends Kt{constructor(e,t,o=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=o,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},yB=class extends Kt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ae(e.value)}}},bB=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Yo.getTensorsInContainer(e.value),o=this.transform(e.value),n=Yo.getTensorsInContainer(o);for(let s of t)Yo.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},wB=class extends Kt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},aI=class extends Kt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Yo.getTensorsInContainer(e.value),o=await this.transform(e.value),n=Yo.getTensorsInContainer(o);for(let s of t)Yo.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},om=class extends Kt{constructor(){super();this.outputQueue=new rm,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},_B=class extends om{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=Yo.getTensorsInContainer(e.value),o=this.transform(e.value),n=Yo.getTensorsInContainer(o);this.outputQueue.pushAll(o);for(let s of t)Yo.isTensorInList(s,n)||s.dispose();return!0}},iI=class extends Kt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let o=await this.moreIterators.next();if(o.done)return{value:null,done:!0};this.iterator=o.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Fa;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(Fa||(Fa={}));var mB=class extends Kt{constructor(e,t=Fa.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,o=0;function n(a){return a instanceof 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Should have ${this.fullColumnNames.length} elements in a row, but got ${o}`);return o}};var xh=class extends Kt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(W().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new xh(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(o){throw new Error(`Error thrown while initializing video stream: ${o.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(y.sizeFromShape(t));return o.set(e,o.length-e.length),Pr(o,t)}};var yh=class extends Kt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Vt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=ri([a,s,l,i],[1,4])}else this.cropBox=ri([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(W().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let o=new yh(e,t);return await o.start(),o}async start(){this.webcamConfig.facingMode&&y.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=rg.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=lr(ne(e,"float32"),0),o;o=oi.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=o.shape;return L(o,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var bh=class{};var by=class extends Kt{split(e){return new TB(this,e)}},TB=class extends by{constructor(e,t){super();this.upstream=e,this.impl=new AB(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},AB=class extends om{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let o of t.slice(0,-1))this.outputQueue.push(o);return this.carryover=t[t.length-1],!0}};var cI=class extends Kt{decodeUTF8(){return new DB(this)}},DB=class extends by{constructor(e){super();this.upstream=e,this.impl=new $B(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},$B=class extends om{constructor(e){super();if(this.upstream=e,W().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=EB();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let o;return W().get("IS_BROWSER")?o=this.decoder.decode(t,{stream:!0}):o=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(o),!0}};var wh=class extends cI{constructor(e,t={}){super();this.file=e,this.options=t,y.assert(e instanceof Uint8Array||(W().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,o)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof 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extends bh{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return wy(this.url)?new _h(this.url,this.fileOptions).iterator():RB(this.url,this.fileOptions)}};function FB(r,e={}){return new gh(new kh(r),e)}function OB(r){let e=fh(r);return wo(async()=>e)}function PB(r){return wo(async()=>{let e=await r();return fh(()=>e.next())})}async function MB(r,e){return yh.create(r,e)}async function LB(r){return xh.create(r)}var zB="3.3.0";var SGt={tfjs:(dm==null?void 0:dm.version)||void 0,"tfjs-core":(hm==null?void 0:hm.version)||void 0,"tfjs-data":(gm==null?void 0:gm.version)||void 0,"tfjs-layers":(xm==null?void 0:xm.version)||void 0,"tfjs-converter":(ym==null?void 0:ym.version)||void 0,"tfjs-backend-cpu":A_||void 0,"tfjs-backend-webgl":zk||void 0,"tfjs-backend-wasm":wC||void 0};export{Os as Abs,yi as Acos,bi as Acosh,ip as AdadeltaOptimizer,ap as AdagradOptimizer,lp as AdamOptimizer,up as AdamaxOptimizer,Fo as Add,kn as AddN,mc as All,fc as Any,vn as ArgMax,Ga as 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FloorDiv,Im as FromPixels,Pn as FusedBatchNorm,Xs as FusedConv2D,Ys as FusedDepthwiseConv2D,tx as GPGPUContext,Ri as GatherNd,Ls as GatherV2,Y0 as GraphModel,Fi as Greater,Mn as GreaterEqual,OC as History,Sc as IFFT,Ko as Identity,Tc as Imag,At as InputSpec,Oi as IsFinite,Pi as IsInf,Mi as IsNan,$s as KernelBackend,Xa as LRN,Ec as LRNGrad,Ox as LayerVariable,Wo as LayersModel,Ln as LeakyRelu,Li as Less,zi as LessEqual,Ac as LinSpace,zn as Log,Bi as Log1p,iS as LogSoftmax,Vi as LogicalAnd,Ll as LogicalNot,zl as LogicalOr,wu as MathBackendCPU,Au as MathBackendWebGL,Bn as Max,Gn as MaxPool,Ya as MaxPool3D,$c as MaxPool3DGrad,Dc as MaxPoolGrad,Rc as MaxPoolWithArgmax,Vn as Maximum,Wn as Mean,jn as Min,Un as Minimum,Za as MirrorPad,Gi as Mod,cp as MomentumOptimizer,Fc as Multinomial,Hn as Multiply,zs as Neg,ji as NonMaxSuppressionV3,Ui as NonMaxSuppressionV4,Hi as NonMaxSuppressionV5,Wi as NotEqual,wS as OP_SCOPE_SUFFIX,qn as OneHot,Bs as OnesLike,zr as Optimizer,Vs as Pack,Kn as PadV2,zre as Pool,Xn as Pow,Yn as Prelu,qi as Prod,pp as RMSPropOptimizer,Eo as RNN,Ja as Range,Kb as Rank,Oc as Real,$n as RealDiv,Ki as Reciprocal,Gt as Reduction,Zn as Relu,Qn as Relu6,Gs as Reshape,Jn as ResizeBilinear,Mc as ResizeBilinearGrad,Qa as ResizeNearestNeighbor,Pc as ResizeNearestNeighborGrad,es as Reverse,oa as RotateWithOffset,ts as Round,rs as Rsqrt,pl as SGDOptimizer,Xi as ScatterNd,Ws as Select,Yi as Selu,$a as Sequential,ns as Sigmoid,Ji as Sign,os as Sin,Zi as Sinh,js as Slice,as as Softmax,Qi as Softplus,el as SpaceToBatchND,Lc as SparseToDense,Us as SplitV,ss as Sqrt,tl as Square,ls as SquaredDifference,Xo as Step,ea as StridedSlice,us as Sub,is as Sum,oo as SymbolicTensor,ta as Tan,cs as Tanh,Ve as Tensor,lt as TensorBuffer,Oo as Tile,ra as TopK,zc as Transform,ps as Transpose,Bc as Unique,Hs as Unpack,rl as UnsortedSegmentSum,nl as Variable,qs as ZerosLike,Ks as _FusedMatMul,It as abs,Fm as acos,Om as acosh,ee as add,vw as addN,ql as all,al as any,ll as argMax,Pm as 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enableProdMode,BT as enclosingPowerOfTwo,Zo as engine,W as env,Io as equal,Km as erf,Zt as exp,lr as expandDims,Xm as expm1,Jc as eye,ba as fft,ma as fill,Aie as findBackend,Eie as findBackendFactory,gs as floor,Hl as floorDiv,fD as forceHalfFloat,rn as fused,Qo as gather,MT as gatherND,og as gather_util,Sie as getBackend,Ub as getGradient,Sm as getKernel,qh as getKernelsForBackend,w2 as gpgpu_util,r4 as grad,o4 as grads,tr as greater,po as greaterEqual,ti as ifft,eu as imag,oi as image,lIe as inTopKAsync,Sz as initializers,UC as input,Tr as io,mu as irfft,Mw as isFinite,Lw as isInf,zw as isNaN,Et as keep,$r as kernel_impls,n3 as layers,fa as leakyRelu,tu as less,zo as lessEqual,g1 as linalg,Bw as linspace,bre as loadGraphModel,nee as loadLayersModel,Ym as localResponseNormalization,ur as log,ru as log1p,Vw as logSigmoid,ou as logSoftmax,Jm as logSumExp,gr as logicalAnd,da as logicalNot,nu as logicalOr,Uw as logicalXor,IRe as losses,je as matMul,JS as math,cr as max,ha as maxPool,Qm as maxPool3d,Hw as maxPoolWithArgmax,eo as maximum,dt as mean,Rm as memory,u3 as metrics,ei as min,ys as minimum,ef as mirrorPad,tf as mod,ree as model,c3 as models,Qc as moments,C0e as movingAverage,P as mul,$4 as multiRNNCell,qw as multinomial,He as neg,df as nextFrame,np as norm,tn as notEqual,fs as oneHot,Ar as ones,rr as onesLike,S as op,M4 as outerProduct,Mr as pad,B4 as pad1d,G4 as pad2d,j4 as pad3d,H4 as pad4d,Kw as pool,Lr as pow,xa as prelu,mw as print,su as prod,Cie as profile,tU as rand,uU as randomGamma,dg as randomNormal,bs as randomUniform,tp as range,Nie as ready,cl as real,rf as reciprocal,Xc as registerBackend,see as registerCallbackConstructor,aS as registerGradient,Bl as registerKernel,Hte as registerOp,p3 as regularizers,Er as relu,au as relu6,Tie as removeBackend,L as reshape,qt as reverse,yU as reverse1d,wU as reverse2d,kU as reverse3d,CU as reverse4d,wa as rfft,of as round,lu as rsqrt,le as scalar,OT as scatterND,ng as scatter_util,uu as selu,nf as separableConv2d,oee as sequential,Q as serialization,wW as setBackend,Die as setPlatform,AJ as setWasmPath,EJ as setWasmPaths,j_ as setWebGLContext,s_ as setdiff1dAsync,Mg as shared,Zr as sigmoid,sf as sign,CRe as signal,cu as sin,pu as sinh,Re as slice,af as slice1d,hg as slice2d,lf as slice3d,rp as slice4d,er as slice_util,ya as softmax,xs as softplus,ga as spaceToBatchND,xg as sparseToDense,vRe as spectral,pr as split,gt as sqrt,Oe as square,fu as squaredDifference,No as squeeze,Bt as stack,ws as step,uf as stridedSlice,ce as sub,ge as sum,Gl as sumOutType,cf as tan,Qs as tanh,Pr as tensor,Vt as tensor1d,ri as tensor2d,gw as tensor3d,YU as tensor4d,ZU as tensor5d,JU as tensor6d,Yo as tensor_util,mT as test_util,V as tidy,Lo as tile,Iie as time,pf as topk,bu as train,Ke as transpose,du as truncatedNormal,op as unique,jre as unregisterGradient,Wre as unregisterKernel,mf as unsortedSegmentSum,mr as unstack,ir as upcastType,y as util,n4 as valueAndGrad,s4 as valueAndGrads,i_ as 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t.functionMap[n].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);let p=u.map(f=>f.id);c.forEach(f=>{!f.kept&&i.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()});let m=await t.functionMap[o].executeFunctionAsync(u,t.tensorArrayMap,t.tensorListMap);l=await m[0].data(),m.forEach(f=>{!f.kept&&i.indexOf(f.id)===-1&&p.indexOf(f.id)===-1&&f.dispose()})}return u}case"LoopCond":{let n=v("pred",r,e,t);return[Rs(n)]}case"Switch":{let n=v("pred",r,e,t),o=v("data",r,e,t);return o.kept||(o=Rs(o)),(await n.data())[0]?[void 0,o]:[o,void 0]}case"Merge":{let n=r.inputNames.find(o=>hr(o,e,t)!==void 0);if(n){let o=hr(n,e,t);return[Rs(o)]}return}case"Enter":{let n=v("frameName",r,e,t),o=v("tensor",r,e,t);return t.enterFrame(n),[Rs(o)]}case"Exit":{let n=v("tensor",r,e,t);return t.exitFrame(),[Rs(n)]}case"NextIteration":{let n=v("tensor",r,e,t);return t.nextIteration(),[Rs(n)]}case"TensorArrayV3":{let 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pB=(r,e,t)=>{switch(r.op){case"Cast":return[oe(v("x",r,e,t),v("dtype",r,e,t))];case"ExpandDims":{let n=v("axis",r,e,t);return[ur(v("x",r,e,t),n)]}case"Squeeze":{let n=v("axis",r,e,t);return[Sn(v("x",r,e,t),n)]}case"Reshape":return[L(v("x",r,e,t),v("shape",r,e,t))];case"MirrorPad":return[sf(v("x",r,e,t),v("padding",r,e,t),v("mode",r,e,t))];case"PadV2":case"Pad":return[Mr(v("x",r,e,t),v("padding",r,e,t),v("constantValue",r,e,t))];case"SpaceToBatchND":{let n=v("blockShape",r,e,t),o=v("paddings",r,e,t);return[_a(v("x",r,e,t),n,o)]}case"BatchToSpaceND":{let n=v("blockShape",r,e,t),o=v("crops",r,e,t);return[da(v("x",r,e,t),n,o)]}case"DepthToSpace":{let n=v("blockSize",r,e,t),o=v("dataFormat",r,e,t).toUpperCase();return[Xm(v("x",r,e,t),n,o)]}case"BroadcastTo":return[ha(v("x",r,e,t),v("shape",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function iI(r,e,t,n){let o=((s,a,i)=>{switch(s.category){case"arithmetic":return V(()=>W3(s,a,i));case"basic_math":return 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mB(r,e,t){let{usedNodes:n,inputs:o}=t,s=[],a=Object.keys(o).map(c=>ln(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{n.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{n.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{n.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&n.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var Yre=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Zre=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Jre=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function aI(r){return Yre.indexOf(r.op)>=0}function Kre(r){return Zre.indexOf(r.op)>=0}function Xre(r){return Jre.indexOf(r.op)>=0}var om=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new 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c}processChildNodes(e,t,n,o,s,a){e.children.forEach(i=>{let[l]=$s(i.name,n);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!hr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!hr(u,o,n))&&(s[l]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[o]=ln(t),s=this.graph.nodes[o];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((l,u)=>a[u]===-1||a[u]===l);x.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&x.assert(n.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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Mismatched at element ${this.count}.`);case Ba.SHORTEST:return{value:null,done:!0};case Ba.LONGEST:default:}return this.count++,{value:s,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},kI=class extends Xt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new gh(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},qB=class extends kI{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=RB.alea(n||x.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}};var hi=class{constructor(){this.size=null}batch(e,t=!0){let n=this;x.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let o;return this.size===Infinity||this.size==null?o=this.size:t?o=Math.ceil(this.size/e):o=Math.floor(this.size/e),_n(async()=>(await n.iterator()).columnMajorBatch(e,t,pne),o)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,_n(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,_n(async()=>(await t.iterator()).filter(o=>V(()=>e(o))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return _n(async()=>(await t.iterator()).map(n=>V(()=>e(n))),this.size)}mapAsync(e){let t=this;return _n(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return _n(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,_n(async()=>{let o=xh(async()=>({value:await t.iterator(),done:!1}));return PB(o.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,_n(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let o=this,s=KB.alea(t||x.now().toString());return _n(async()=>{let a=s.int32();return n&&(a+=s.int32()),(await o.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,_n(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};hi.MAX_BUFFER_SIZE=1e4;function _n(r,e=null){return new class extends hi{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function XB(r){return _n(async()=>bI(r),r.length)}function YB(r){if(!Pl(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t<r.length;t++)e=e==null?r[t].size:Math.min(e,r[t].size);else if(r instanceof Object)for(let t in r)e=e==null?r[t].size:Math.min(e,r[t].size);return _n(async()=>{let t=await wy(r,n=>{if(n instanceof hi)return{value:n.iterator(),recurse:!1};if(Pl(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return LB(t,Ba.SHORTEST)},e)}function pne(r){if(r===null)return null;let e=r[0];return DB(e)?{value:mne(r),recurse:!1}:{value:null,recurse:!0}}function mne(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Oe?Bt(r):Pr(r)}var yh=class extends hi{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(o=>(o.endsWith("\r")&&(o=o.slice(0,-1)),o))}};var _y='"',bh=Symbol("out"),ZB=Symbol("field"),ky=Symbol("quote"),vI=Symbol("quoteafterquote"),JB=Symbol("quoteinquote"),wh=class extends hi{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new yh(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(x.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&x.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((o,s)=>(o[s]=o[s]+1||1,o),{}),n=Object.keys(t).filter(o=>t[o]>1);if(x.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let o of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(o)===-1)throw new Error('The key "'+o+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},o={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?o[a]=u:n[a]=u}}return Object.keys(o).length===0?n:{xs:n,ys:o}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],o=0,s=e.length,a=bh;for(let i=0;i<s;i++)switch(a){case bh:switch(e.charAt(i)){case _y:o=i+1,a=ky;break;case this.delimiter:if(o=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=bh;break;default:a=ZB,o=i;break}break;case ZB:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i)),a=bh,o=i+1;break;default:}break;case ky:switch(e.charAt(i)){case _y:a=vI;break;default:}break;case vI:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(o,i-1)),a=bh,o=i+1;break;case _y:a=ky;break;default:a=JB;break}break;case JB:switch(e.charAt(i)){case _y:a=ky;break;default:}break;default:}if(a===vI?n.push(e.substring(o,s-1)):n.push(e.substring(o)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}};var _h=class extends Xt{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(W().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new _h(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let o=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(o,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let o=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(o,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(o=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&o({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(s),o({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((o,s)=>n.set(o,s*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(x.sizeFromShape(t));return n.set(e,n.length-e.length),Pr(n,t)}};var kh=class extends Xt{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Vt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,o=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-n)/2,a=(1-o)/2,i=s+n,l=o+a;this.cropBox=si([a,s,l,i],[1,4])}else this.cropBox=si([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(W().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new kh(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&x.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=ig.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=ur(oe(e,"float32"),0),n;n=ii.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let o=n.shape;return L(n,o.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}};var vh=class{};var vy=class extends Xt{split(e){return new QB(this,e)}},QB=class extends vy{constructor(e,t){super();this.upstream=e,this.impl=new eV(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},eV=class extends im{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}};var CI=class extends Xt{decodeUTF8(){return new rV(this)}},rV=class extends vy{constructor(e){super();this.upstream=e,this.impl=new nV(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},nV=class extends im{constructor(e){super();if(this.upstream=e,W().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=tV();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return W().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}};var Ch=class extends CI{constructor(e,t={}){super();this.file=e,this.options=t,x.assert(e instanceof Uint8Array||(W().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let o=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,o)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>n(new Error("Aborted")),s.onerror=i=>n(new Error(i.type));let a=this.file.slice(this.offset,o);s.readAsArrayBuffer(a)}this.offset=o}),done:!1}}};async function oV(r,e={}){let t,n;typeof r=="string"?t=r:(t=r.url,n=fne(r));let o=await x.fetch(t,n);if(o.ok){let s=new Uint8Array(await o.arrayBuffer());return new Ch(s,e)}else throw new Error(o.statusText)}var fne=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function Cy(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var Ih=class extends vh{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(Cy(this.input)&&W().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new Ch(this.input,this.options)}};var Nh=class extends vh{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return Cy(this.url)?new Ih(this.url,this.fileOptions).iterator():oV(this.url,this.fileOptions)}};function sV(r,e={}){return new wh(new Nh(r),e)}function iV(r){let e=xh(r);return _n(async()=>e)}function aV(r){return _n(async()=>{let e=await r();return xh(()=>e.next())})}async function lV(r,e){return kh.create(r,e)}async function uV(r){return _h.create(r)}var cV="3.4.0";var QWt={tfjs:(ym==null?void 0:ym.version)||void 0,"tfjs-core":(bm==null?void 0:bm.version)||void 0,"tfjs-data":(wm==null?void 0:wm.version)||void 0,"tfjs-layers":(_m==null?void 0:_m.version)||void 0,"tfjs-converter":(km==null?void 0:km.version)||void 0,"tfjs-backend-cpu":W_||void 0,"tfjs-backend-webgl":Qk||void 0,"tfjs-backend-wasm":FC||void 0};export{Ls as Abs,_i as Acos,ki as Acosh,up as AdadeltaOptimizer,cp as AdagradOptimizer,pp as AdamOptimizer,mp as AdamaxOptimizer,On as Add,wo as AddN,vi as All,Ci as Any,_o as ArgMax,Ka as ArgMin,Ii as Asin,Ni as Asinh,Si as Atan,Ai as Atan2,Ti as Atanh,ko as AvgPool,Xa as AvgPool3D,xc as AvgPool3DGrad,gc as AvgPoolGrad,Ix as BackendWasm,vo as BatchMatMul,Ya as BatchToSpaceND,yc as Bincount,bS as BroadcastTo,O0 as Callback,KC as CallbackList,qn as Cast,Co as Ceil,Kn as ClipByValue,bc as Complex,Za as ComplexAbs,zs as Concat,Io as Conv2D,wc as Conv2DBackpropFilter,No as Conv2DBackpropInput,Ja as Conv3D,_c as Conv3DBackpropFilterV2,kc as Conv3DBackpropInputV2,So as Cos,Ei as Cosh,Di as CropAndResize,To as Cumsum,YC as CustomCallback,qa as DataStorage,vc as DenseBincount,$i as DepthToSpace,Ao as DepthwiseConv2dNative,Cc as DepthwiseConv2dNativeBackpropFilter,Ic as DepthwiseConv2dNativeBackpropInput,Nc as Diag,Qa as Dilation2D,Tm as Dilation2DBackpropFilter,Sm as Dilation2DBackpropInput,Qb as ENV,P0 as EarlyStopping,Sc as Einsum,Ri as Elu,Tc as EluGrad,Yh as Environment,Oi as Equal,Fi as Erf,Do as Exp,Bs as ExpandDims,Pi as Expm1,Ac as FFT,el as Fill,Mi as FlipLeftRight,$o as Floor,Ro as FloorDiv,Am as FromPixels,Fo as FusedBatchNorm,Js as FusedConv2D,Qs as FusedDepthwiseConv2D,sx as GPGPUContext,Li as GatherNd,Vs as GatherV2,cI as GraphModel,zi as Greater,Oo as GreaterEqual,XC as History,Ec as IFFT,Xn as Identity,Dc as Imag,Ct as InputSpec,Bi as IsFinite,Vi as IsInf,Gi as IsNan,Os as KernelBackend,tl as LRN,Rc as LRNGrad,zx as LayerVariable,jn as LayersModel,Po as LeakyRelu,Wi as Less,ji as LessEqual,$c as LinSpace,Mo as Log,Ui as Log1p,wS as LogSoftmax,Hi as LogicalAnd,jl as LogicalNot,Ul as LogicalOr,Nu as MathBackendCPU,Ou as MathBackendWebGL,Lo as Max,Bo as MaxPool,rl as MaxPool3D,Oc as MaxPool3DGrad,Fc as MaxPoolGrad,Pc as MaxPoolWithArgmax,zo as Maximum,Vo as Mean,Go as Min,Wo as Minimum,jo as MirrorPad,qi as Mod,fp as MomentumOptimizer,Mc as Multinomial,Uo as Multiply,Gs as Neg,Xi as NonMaxSuppressionV3,Yi as NonMaxSuppressionV4,Zi as NonMaxSuppressionV5,Ki as NotEqual,FS as OP_SCOPE_SUFFIX,Ho as OneHot,Ws as OnesLike,zr as Optimizer,js as Pack,qo as PadV2,_ne as Pool,Ko as Pow,Xo as Prelu,Ji as Prod,dp as RMSPropOptimizer,Dn as RNN,nl as Range,iw as Rank,Lc as Real,Eo as RealDiv,Qi as Reciprocal,Gt as Reduction,Yo as Relu,Jo as Relu6,Us as Reshape,Zo as ResizeBilinear,Bc as ResizeBilinearGrad,ol as ResizeNearestNeighbor,zc as ResizeNearestNeighborGrad,Qo as Reverse,aa as RotateWithOffset,es as Round,ts as Rsqrt,hl as SGDOptimizer,ea as ScatterNd,Hs as Select,ta as Selu,La as Sequential,ns as Sigmoid,na as Sign,rs as Sin,ra as Sinh,qs as Slice,is as Softmax,oa as Softplus,sl as SpaceToBatchND,Vc as SparseToDense,Ks as SplitV,os as Sqrt,il as Square,as as SquaredDifference,Yn as Step,sa as StridedSlice,ls as Sub,ss as Sum,sn as SymbolicTensor,us as Tan,cs as Tanh,Oe as Tensor,ut as TensorBuffer,Pn as Tile,ia as TopK,Gc as Transform,ps as Transpose,Wc as Unique,Xs as Unpack,al as UnsortedSegmentSum,ul as Variable,Ys as ZerosLike,Zs as _FusedMatMul,Nt as abs,Lm as acos,zm as acosh,ee as add,Ow as addN,Ql as all,ml as any,fl as argMax,Bm as argMin,Vm as asin,Gm as asinh,Wm as atan,jm as atan2,Um as atanh,fa as avgPool,Hm as avgPool3d,AT as backend,C as backend_util,xj as basicLSTMCell,Jn as batchNorm,zw as batchNorm2d,Bw as batchNorm3d,Vw as batchNorm4d,da as batchToSpaceND,Gw as bincount,U0e as booleanMaskAsync,ha as broadcastTo,ig as browser,Ce as buffer,Sre as callbacks,oe as cast,qm as ceil,lr as clipByValue,Mn as clone,In as complex,Qe as concat,Ww as concat1d,jw as concat2d,Uw as concat3d,Hw as concat4d,Dz as constraints,ru as conv1d,Jr as conv2d,nu as conv2dTranspose,Km as conv3d,qw as conv3dTranspose,Sne as copyRegisteredKernels,ga as cos,ou as cosh,kg as cosineWindow,su as cumsum,Qr as customGrad,II as data,Kw as denseBincount,Fw as deprecationWarn,Xm as depthToSpace,hs as depthwiseConv2d,Are as deregisterOp,Yl as device_util,Uj as diag,Ym as dilation2d,oae as disableDeprecationWarnings,Ae as dispose,sae as disposeVariables,me as div,Zm as divNoNan,Xw as dot,QT as dropout,Yw as einsum,gs as elu,nae as enableDebugMode,rae as enableProdMode,e1 as enclosingPowerOfTwo,ds as engine,W as env,Nn as equal,Jm as erf,Jt as exp,ur as expandDims,Qm as expm1,tp as eye,Ca as fft,xa as fill,pae as findBackend,mae as findBackendFactory,xs as floor,Jl as floorDiv,ED as forceHalfFloat,ro as fused,Qn as gather,ZT as gatherND,ag as gather_util,uae as getBackend,nw as getGradient,Dm as getKernel,Zh as getKernelsForBackend,M2 as gpgpu_util,_4 as grad,k4 as grads,nr as greater,dn as greaterEqual,oi as ifft,iu as imag,ii as image,tNe as inTopKAsync,Jz as initializers,s0 as input,Tr as io,yu as irfft,Zw as isFinite,Jw as isInf,ef as isNaN,Et as keep,Dr as kernel_impls,A3 as layers,ya as leakyRelu,au as less,Bn as lessEqual,E1 as linalg,Qw as linspace,tne as loadGraphModel,zee as loadLayersModel,tf as localResponseNormalization,cr as log,lu as log1p,e_ as logSigmoid,uu as logSoftmax,nf as logSumExp,xr as logicalAnd,ba as logicalNot,cu as logicalOr,o_ as logicalXor,yFe as losses,ze as matMul,mT as math,pr as max,wa as maxPool,of as maxPool3d,s_ as maxPoolWithArgmax,en as maximum,dt as mean,Mm as memory,j4 as meshgrid,F3 as metrics,ni as min,bs as minimum,sf as mirrorPad,af as mod,Mee as model,O3 as models,rp as moments,xIe as movingAverage,P as mul,J4 as multiRNNCell,i_ as multinomial,qe as neg,bf as nextFrame,ap as norm,to as notEqual,fs as oneHot,Qt as ones,or as onesLike,S as op,nU as outerProduct,Mr as pad,iU as pad1d,lU as pad2d,cU as pad3d,mU as pad4d,a_ as pool,Lr as pow,ka as prelu,Cw as print,pu as prod,iae as profile,_U as rand,AU as randomGamma,yg as randomNormal,ws as randomUniform,op as range,lae as ready,dl as real,lf as reciprocal,Jc as registerBackend,Bee as registerCallbackConstructor,_S as registerGradient,Hl as registerKernel,Tre as registerOp,P3 as regularizers,Ar as relu,fu as relu6,cae as removeBackend,L as reshape,qt as reverse,LU as reverse1d,BU as reverse2d,GU as reverse3d,jU as reverse4d,Ia as rfft,uf as round,du as rsqrt,le as scalar,XT as scatterND,lg as scatter_util,hu as selu,cf as separableConv2d,Lee as sequential,J as serialization,zW as setBackend,fae as setPlatform,cQ as setWasmPath,pQ as setWasmPaths,ok as setWebGLContext,y_ as setdiff1dAsync,Vg as shared,Zr as sigmoid,pf as sign,xFe as signal,gu as sin,xu as sinh,Fe as slice,mf as slice1d,bg as slice2d,ff as slice3d,sp as slice4d,rr as slice_util,va as softmax,ys as softplus,_a as spaceToBatchND,_g as sparseToDense,gFe as spectral,mr as split,gt as sqrt,Pe as square,bu as squaredDifference,Sn as squeeze,Bt as stack,_s as step,df as stridedSlice,pe as sub,ge as sum,Kl as sumOutType,hf as tan,ri as tanh,Pr as tensor,Vt as tensor1d,si as tensor2d,Tw as tensor3d,gH as tensor4d,xH as tensor5d,yH as tensor6d,Zn as tensor_util,NT as test_util,V as tidy,zn as tile,aae as time,gf as topk,Iu as train,Ue as transpose,wu as truncatedNormal,ip as unique,Nne as unregisterGradient,Ine as unregisterKernel,xf as unsortedSegmentSum,fr as unstack,ar as upcastType,x as util,v4 as valueAndGrad,C4 as valueAndGrads,b_ as variable,hg as variableGrads,QWt as version,rne as version_converter,LW as version_core,W_ as version_cpu,dd as version_layers,FC as version_wasm,Qk as version_webgl,IXe as webgl,$2 as webgl_util,Dt as where,yf as whereAsync,ht as zeros,Ie as zerosLike};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/tfjs.esm.js.map b/dist/tfjs.esm.js.map
index 415ff3c7..f62104c9 100644
--- a/dist/tfjs.esm.js.map
+++ b/dist/tfjs.esm.js.map
@@ -1,7 +1,7 @@
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-  "sourcesContent": ["", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 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Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw 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ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"F\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={8991:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i<pthreadPoolSize;++i){PThread.allocateUnusedWorker()}},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();var tb=_pthread_self();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}function ___call_main(argc,argv){var returnCode=_main(argc,argv)}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function _emscripten_set_thread_name(threadId,name){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"x\":__emscripten_notify_thread_queue,\"b\":_abort,\"y\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"z\":_emscripten_num_logical_cores,\"u\":_emscripten_receive_on_main_thread_js,\"q\":_emscripten_resize_heap,\"v\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"t\":_emscripten_set_thread_name,\"w\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"s\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var 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cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=Module[\"asm\"][\"g\"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module[\"asm\"][\"m\"];removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _abort(){abort()}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function _emscripten_get_heap_size(){return HEAPU8.length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,HEAPU8[ptr+j])}num+=len}HEAP32[pnum>>2]=num;return 0}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"h\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"i\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"j\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"k\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"l\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"n\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"o\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"p\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"q\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"r\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"s\"]).apply(null,arguments)};var _Ceil=Module[\"_Ceil\"]=function(){return(_Ceil=Module[\"_Ceil\"]=Module[\"asm\"][\"t\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"u\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"v\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"w\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"x\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"z\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"A\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"B\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var 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_Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var 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_Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Da\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ea\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"Fa\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ga\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Ha\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1250, moduleBytes: 4013323, imports: 7, importBytes: 2255, outputBytes: 2991826, outputFiles: 'dist/tfjs.esm.js' }\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1253, moduleBytes: 4029357, imports: 7, importBytes: 2285, outputBytes: 2998298, outputFiles: 'dist/tfjs.esm.js' }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - compiled\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - sources\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export versions\nexport const version = {\n  tfjs: packageBundle?.version || undefined,\n  'tfjs-core': packageCore?.version || undefined,\n  'tfjs-data': packageData?.version || undefined,\n  'tfjs-layers': packageLayers?.version || undefined,\n  'tfjs-converter': packageConverter?.version || undefined,\n  'tfjs-backend-cpu': version_cpu || undefined,\n  'tfjs-backend-webgl': version_webgl || undefined,\n  'tfjs-backend-wasm': version_wasm || undefined,\n};\n// export const version = {};\n"],
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+  "sourcesContent": ["", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModule = (function() {\n  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n  if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n  return (\nfunction(WasmBackendModule) {\n  WasmBackendModule = WasmBackendModule || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var readyPromiseResolve,readyPromiseReject;Module[\"ready\"]=new Promise(function(resolve,reject){readyPromiseResolve=resolve;readyPromiseReject=reject});var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}global.Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!==\"undefined\"&&document.currentScript){scriptDirectory=document.currentScript.src}if(typeof _scriptDir !== \"undefined\" && _scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){global.performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime=Module[\"noExitRuntime\"]||true;if(typeof WebAssembly!==\"object\"){abort(\"no native wasm support detected\")}var wasmMemory;var wasmModule;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type===\"number\"});var numericRet=returnType!==\"string\";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var str=\"\";while(!(idx>=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_MEMORY/65536,\"maximum\":2147483648/65536,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){if(ENVIRONMENT_IS_PTHREAD)return;runtimeExited=true}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";var e=new WebAssembly.RuntimeError(what);readyPromiseReject(e);throw e}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmTable=Module[\"asm\"][\"D\"];wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){removeRunDependency(\"wasm-instantiate\")}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");return instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync().catch(readyPromiseReject);return{}}var ASM_CONSTS={9800:function(){throw\"Canceled!\"},9818:function($0,$1){setTimeout(function(){__emscripten_do_dispatch_to_thread($0,$1)},0)}};function initPthreadsJS(){PThread.initRuntime()}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){wasmTable.get(func)()}else{wasmTable.get(func)(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function killThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! killThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in killThread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function cancelThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cancelThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cancelThread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function cleanupThread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! cleanupThread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in cleanupThread!\";var pthread=PThread.pthreads[pthread_ptr];if(pthread){GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={unusedWorkers:[],runningWorkers:[],initMainThreadBlock:function(){},initRuntime:function(){var tb=_malloc(228);for(var i=0;i<228/4;++i)GROWABLE_HEAP_U32()[tb/4+i]=0;GROWABLE_HEAP_I32()[tb+12>>2]=tb;var headPtr=tb+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=_malloc(512);for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),tb+100>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tb+40>>2,tb);__emscripten_thread_init(tb,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(tb)},initWorker:function(){},pthreads:{},threadExitHandlers:[],setThreadStatus:function(){},runExitHandlers:function(){while(PThread.threadExitHandlers.length>0){PThread.threadExitHandlers.pop()()}if(ENVIRONMENT_IS_PTHREAD&&_pthread_self())___pthread_tsd_run_dtors()},runExitHandlersAndDeinitThread:function(tb,exitCode){Atomics.store(GROWABLE_HEAP_U32(),tb+56>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,0);PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);_emscripten_futex_wake(tb+0,2147483647);__emscripten_thread_init(0,0,0)},threadExit:function(exitCode){var tb=_pthread_self();if(tb){PThread.runExitHandlersAndDeinitThread(tb,exitCode);if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlersAndDeinitThread(_pthread_self(),-1);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i<PThread.unusedWorkers.length;++i){var worker=PThread.unusedWorkers[i];worker.terminate()}PThread.unusedWorkers=[];for(var i=0;i<PThread.runningWorkers.length;++i){var worker=PThread.runningWorkers[i];var pthread=worker.pthread;PThread.freeThreadData(pthread);worker.terminate()}PThread.runningWorkers=[]},freeThreadData:function(pthread){if(!pthread)return;if(pthread.threadInfoStruct){var tlsMemory=GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+100>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){PThread.runWithoutMainThreadQueuedCalls(function(){delete PThread.pthreads[worker.pthread.threadInfoStruct];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined})},runWithoutMainThreadQueuedCalls:function(func){GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=0;try{func()}finally{GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls>>2]=1}},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){spawnThread(e.data)}else if(cmd===\"cleanupThread\"){cleanupThread(d[\"thread\"])}else if(cmd===\"killThread\"){killThread(d[\"thread\"])}else if(cmd===\"cancelThread\"){cancelThread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.threadInfoStruct+64>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"exitProcess\"){try{exit(d[\"returnCode\"])}catch(e){if(e instanceof ExitStatus)return;throw e}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()<t){}}};function establishStackSpace(stackTop,stackMax){_emscripten_stack_set_limits(stackTop,stackMax);stackRestore(stackTop)}Module[\"establishStackSpace\"]=establishStackSpace;function getNoExitRuntime(){return noExitRuntime}Module[\"getNoExitRuntime\"]=getNoExitRuntime;function invokeEntryPoint(ptr,arg){return wasmTable.get(ptr)(arg)}Module[\"invokeEntryPoint\"]=invokeEntryPoint;function ___assert_fail(condition,filename,line,func){abort(\"Assertion failed: \"+UTF8ToString(condition)+\", at: \"+[filename?UTF8ToString(filename):\"unknown filename\",line,func?UTF8ToString(func):\"unknown function\"])}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process[\"hrtime\"]();return t[0]*1e3+t[1]/1e6}}else if(ENVIRONMENT_IS_PTHREAD){_emscripten_get_now=function(){return performance.now()-Module[\"__performance_now_clock_drift\"]}}else if(typeof dateNow!==\"undefined\"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function setErrNo(value){GROWABLE_HEAP_I32()[___errno_location()>>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg)}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_asm_const_int(code,sigPtr,argbuf){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(!ENVIRONMENT_IS_WEB){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}var tNow=performance.now();var tEnd=tNow+timeout;var lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr);while(1){tNow=performance.now();if(tNow>tEnd){lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);return-73}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,0);if(lastAddr==0){break}_emscripten_main_thread_process_queued_calls();if(Atomics.load(GROWABLE_HEAP_I32(),addr>>2)!=val){return-6}lastAddr=Atomics.exchange(GROWABLE_HEAP_I32(),__emscripten_main_thread_futex>>2,addr)}return 0}}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){if(ENVIRONMENT_IS_NODE)return require(\"os\").cpus().length;return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var serializedNumCallArgs=numCallArgs;var args=stackAlloc(serializedNumCallArgs*8);var b=args>>3;for(var i=0;i<numCallArgs;i++){var arg=arguments[2+i];GROWABLE_HEAP_F64()[b+i]=arg}var ret=_emscripten_run_in_main_runtime_thread_js(index,serializedNumCallArgs,args,sync);stackRestore(stack);return ret}var _emscripten_receive_on_main_thread_js_callArgs=[];var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=GROWABLE_HEAP_U8()[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?GROWABLE_HEAP_F64()[buf++>>1]:GROWABLE_HEAP_I32()[buf]);++buf}return readAsmConstArgsArray}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i<numCallArgs;i++){_emscripten_receive_on_main_thread_js_callArgs[i]=GROWABLE_HEAP_F64()[b+i]}var isEmAsmConst=index<0;var func=!isEmAsmConst?proxiedFunctionTable[index]:ASM_CONSTS[-index-1];return func.apply(null,_emscripten_receive_on_main_thread_js_callArgs)}function _emscripten_get_heap_size(){return GROWABLE_HEAP_U8().length}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},queueEventHandlerOnThread_iiii:function(targetThread,eventHandlerFunc,eventTypeId,eventData,userData){var stackTop=stackSave();var varargs=stackAlloc(12);GROWABLE_HEAP_I32()[varargs>>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;__emscripten_call_on_thread(0,targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;__emscripten_call_on_thread(0,targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension(\"WEBGL_multi_draw\"))}var GL={counter:1,buffers:[],programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},getSource:function(shader,count,string,length){var source=\"\";for(var i=0;i<count;++i){var len=length?GROWABLE_HEAP_I32()[length+i*4>>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(ext.indexOf(\"lose_context\")<0&&ext.indexOf(\"debug\")<0){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i<numUniforms;++i){var u=GLctx.getActiveUniform(p,i);var name=u.name;ptable.maxUniformLength=Math.max(ptable.maxUniformLength,name.length+1);if(name.slice(-1)==\"]\"){name=name.slice(0,name.lastIndexOf(\"[\"))}var loc=GLctx.getUniformLocation(p,name);if(loc){var id=GL.getNewId(GL.uniforms);utable[name]=[u.size,id];GL.uniforms[id]=loc;for(var j=1;j<u.size;++j){var n=name+\"[\"+j+\"]\";loc=GLctx.getUniformLocation(p,n);id=GL.getNewId(GL.uniforms);GL.uniforms[id]=loc}}}}};var __emscripten_webgl_power_preferences=[\"default\",\"low-power\",\"high-performance\"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];var contextAttributes={\"alpha\":!!GROWABLE_HEAP_I32()[a+(0>>2)],\"depth\":!!GROWABLE_HEAP_I32()[a+(4>>2)],\"stencil\":!!GROWABLE_HEAP_I32()[a+(8>>2)],\"antialias\":!!GROWABLE_HEAP_I32()[a+(12>>2)],\"premultipliedAlpha\":!!GROWABLE_HEAP_I32()[a+(16>>2)],\"preserveDrawingBuffer\":!!GROWABLE_HEAP_I32()[a+(20>>2)],\"powerPreference\":__emscripten_webgl_power_preferences[powerPreference],\"failIfMajorPerformanceCaveat\":!!GROWABLE_HEAP_I32()[a+(28>>2)],majorVersion:GROWABLE_HEAP_I32()[a+(32>>2)],minorVersion:GROWABLE_HEAP_I32()[a+(36>>2)],enableExtensionsByDefault:GROWABLE_HEAP_I32()[a+(40>>2)],explicitSwapControl:GROWABLE_HEAP_I32()[a+(44>>2)],proxyContextToMainThread:GROWABLE_HEAP_I32()[a+(48>>2)],renderViaOffscreenBackBuffer:GROWABLE_HEAP_I32()[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i<iovcnt;i++){var ptr=GROWABLE_HEAP_I32()[iov+i*8>>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j<len;j++){SYSCALLS.printChar(fd,GROWABLE_HEAP_U8()[ptr+j])}num+=len}GROWABLE_HEAP_I32()[pnum>>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.threadExitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){PThread.threadExitHandlers.push(function(){wasmTable.get(routine)(arg)})}function spawnThread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! spawnThread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(64>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(100>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(76>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(104+12>>2),threadParams.detached);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(172>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;if(attr&&attr!=-1){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(228);for(var i=0;i<228>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+152;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{spawnThread(threadParams)}return 0}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:case 80:case 81:case 79:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();var GLctx;var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"v\":__emscripten_notify_thread_queue,\"b\":_abort,\"w\":_emscripten_asm_const_int,\"j\":_emscripten_conditional_set_current_thread_status,\"c\":_emscripten_futex_wait,\"d\":_emscripten_futex_wake,\"f\":_emscripten_get_now,\"p\":_emscripten_memcpy_big,\"x\":_emscripten_num_logical_cores,\"s\":_emscripten_receive_on_main_thread_js,\"r\":_emscripten_resize_heap,\"t\":_emscripten_set_canvas_element_size,\"i\":_emscripten_set_current_thread_status,\"u\":_emscripten_webgl_create_context,\"m\":_fd_close,\"n\":_fd_seek,\"g\":_fd_write,\"o\":initPthreadsJS,\"a\":wasmMemory||Module[\"wasmMemory\"],\"k\":_pthread_cleanup_pop,\"l\":_pthread_cleanup_push,\"h\":_pthread_create,\"q\":_sysconf};var asm=createWasm();var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"y\"]).apply(null,arguments)};var 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_Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var 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_Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _MirrorPad=Module[\"_MirrorPad\"]=function(){return(_MirrorPad=Module[\"_MirrorPad\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Multiply=Module[\"_Multiply\"]=function(){return(_Multiply=Module[\"_Multiply\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _Neg=Module[\"_Neg\"]=function(){return(_Neg=Module[\"_Neg\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var 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read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof 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TextDecoder!==\"undefined\"?new TextDecoder(\"utf8\"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var 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8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}var asmLibraryArg={\"a\":_abort,\"d\":_emscripten_memcpy_big,\"e\":_emscripten_resize_heap,\"f\":_fd_close,\"c\":_fd_seek,\"b\":_fd_write,\"g\":_pthread_create,\"h\":_sysconf};var asm=createWasm();var 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_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=function(){return(_NonMaxSuppressionV5=Module[\"_NonMaxSuppressionV5\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _NotEqual=Module[\"_NotEqual\"]=function(){return(_NotEqual=Module[\"_NotEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _OneHot=Module[\"_OneHot\"]=function(){return(_OneHot=Module[\"_OneHot\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _PadV2=Module[\"_PadV2\"]=function(){return(_PadV2=Module[\"_PadV2\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Pow=Module[\"_Pow\"]=function(){return(_Pow=Module[\"_Pow\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _Prelu=Module[\"_Prelu\"]=function(){return(_Prelu=Module[\"_Prelu\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Prod=Module[\"_Prod\"]=function(){return(_Prod=Module[\"_Prod\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _RealDiv=Module[\"_RealDiv\"]=function(){return(_RealDiv=Module[\"_RealDiv\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Relu=Module[\"_Relu\"]=function(){return(_Relu=Module[\"_Relu\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Relu6=Module[\"_Relu6\"]=function(){return(_Relu6=Module[\"_Relu6\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var _ResizeBilinear=Module[\"_ResizeBilinear\"]=function(){return(_ResizeBilinear=Module[\"_ResizeBilinear\"]=Module[\"asm\"][\"qa\"]).apply(null,arguments)};var _Reverse=Module[\"_Reverse\"]=function(){return(_Reverse=Module[\"_Reverse\"]=Module[\"asm\"][\"ra\"]).apply(null,arguments)};var _RotateWithOffset=Module[\"_RotateWithOffset\"]=function(){return(_RotateWithOffset=Module[\"_RotateWithOffset\"]=Module[\"asm\"][\"sa\"]).apply(null,arguments)};var _Round=Module[\"_Round\"]=function(){return(_Round=Module[\"_Round\"]=Module[\"asm\"][\"ta\"]).apply(null,arguments)};var _Rsqrt=Module[\"_Rsqrt\"]=function(){return(_Rsqrt=Module[\"_Rsqrt\"]=Module[\"asm\"][\"ua\"]).apply(null,arguments)};var _ScatterNd=Module[\"_ScatterNd\"]=function(){return(_ScatterNd=Module[\"_ScatterNd\"]=Module[\"asm\"][\"va\"]).apply(null,arguments)};var _SelectV2=Module[\"_SelectV2\"]=function(){return(_SelectV2=Module[\"_SelectV2\"]=Module[\"asm\"][\"wa\"]).apply(null,arguments)};var _Sigmoid=Module[\"_Sigmoid\"]=function(){return(_Sigmoid=Module[\"_Sigmoid\"]=Module[\"asm\"][\"xa\"]).apply(null,arguments)};var _Sin=Module[\"_Sin\"]=function(){return(_Sin=Module[\"_Sin\"]=Module[\"asm\"][\"ya\"]).apply(null,arguments)};var _Softmax=Module[\"_Softmax\"]=function(){return(_Softmax=Module[\"_Softmax\"]=Module[\"asm\"][\"za\"]).apply(null,arguments)};var _Sqrt=Module[\"_Sqrt\"]=function(){return(_Sqrt=Module[\"_Sqrt\"]=Module[\"asm\"][\"Aa\"]).apply(null,arguments)};var 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_Tanh=Module[\"_Tanh\"]=function(){return(_Tanh=Module[\"_Tanh\"]=Module[\"asm\"][\"Ia\"]).apply(null,arguments)};var _Tile=Module[\"_Tile\"]=function(){return(_Tile=Module[\"_Tile\"]=Module[\"asm\"][\"Ja\"]).apply(null,arguments)};var _TopK=Module[\"_TopK\"]=function(){return(_TopK=Module[\"_TopK\"]=Module[\"asm\"][\"Ka\"]).apply(null,arguments)};var _Transpose=Module[\"_Transpose\"]=function(){return(_Transpose=Module[\"_Transpose\"]=Module[\"asm\"][\"La\"]).apply(null,arguments)};var __FusedMatMul=Module[\"__FusedMatMul\"]=function(){return(__FusedMatMul=Module[\"__FusedMatMul\"]=Module[\"asm\"][\"Ma\"]).apply(null,arguments)};var _malloc=Module[\"_malloc\"]=function(){return(_malloc=Module[\"_malloc\"]=Module[\"asm\"][\"Na\"]).apply(null,arguments)};var _free=Module[\"_free\"]=function(){return(_free=Module[\"_free\"]=Module[\"asm\"][\"Oa\"]).apply(null,arguments)};var ___errno_location=Module[\"___errno_location\"]=function(){return(___errno_location=Module[\"___errno_location\"]=Module[\"asm\"][\"Pa\"]).apply(null,arguments)};var stackSave=Module[\"stackSave\"]=function(){return(stackSave=Module[\"stackSave\"]=Module[\"asm\"][\"Qa\"]).apply(null,arguments)};var stackRestore=Module[\"stackRestore\"]=function(){return(stackRestore=Module[\"stackRestore\"]=Module[\"asm\"][\"Ra\"]).apply(null,arguments)};var stackAlloc=Module[\"stackAlloc\"]=function(){return(stackAlloc=Module[\"stackAlloc\"]=Module[\"asm\"][\"Sa\"]).apply(null,arguments)};Module[\"cwrap\"]=cwrap;var calledRun;function ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();readyPromiseResolve(Module);if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}run();\n\n\n  return WasmBackendModule.ready\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n  module.exports = WasmBackendModule;\nelse if (typeof define === 'function' && define['amd'])\n  define([], function() { return WasmBackendModule; });\nelse if (typeof exports === 'object')\n  exports[\"WasmBackendModule\"] = WasmBackendModule;\n", "// A port of an algorithm by Johannes Baag\u00F8e <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = String(data);\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n} else {\n  // When included as a plain script, set up Math.seedrandom global.\n  math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n  // global: `self` in browsers (including strict mode and web workers),\n  // otherwise `this` in Node and other environments\n  (typeof self !== 'undefined') ? self : this,\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};function moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModule(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"getNoExitRuntime\"]()){}else{Module[\"PThread\"].threadExit(ex.status)}}else{Module[\"PThread\"].threadExit(-2);throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1250, moduleBytes: 4013323, imports: 7, importBytes: 2255, outputBytes: 2991826, outputFiles: 'dist/tfjs.esm.js' }\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1253, moduleBytes: 4029357, imports: 7, importBytes: 2285, outputBytes: 2998298, outputFiles: 'dist/tfjs.esm.js' }\n\n// get versions of all packages.\nimport * as packageBundle from '@tensorflow/tfjs/package.json';\nimport * as packageCore from '@tensorflow/tfjs-core/package.json';\nimport * as packageData from '@tensorflow/tfjs-data/package.json';\nimport * as packageLayers from '@tensorflow/tfjs-layers/package.json';\nimport * as packageConverter from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - compiled\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all - sources\n/*\nexport * from '@tensorflow/tfjs-core/src/index';\nexport * from '@tensorflow/tfjs-layers/src/index';\nexport * from '@tensorflow/tfjs-converter/src/index';\nexport * as data from '@tensorflow/tfjs-data/src/index';\nexport * from '@tensorflow/tfjs-backend-cpu/src/index';\nexport * from '@tensorflow/tfjs-backend-webgl/src/index';\nexport * from '@tensorflow/tfjs-backend-wasm/src/index';\n*/\n\n// export versions\nexport const version = {\n  tfjs: packageBundle?.version || undefined,\n  'tfjs-core': packageCore?.version || undefined,\n  'tfjs-data': packageData?.version || undefined,\n  'tfjs-layers': packageLayers?.version || undefined,\n  'tfjs-converter': packageConverter?.version || undefined,\n  'tfjs-backend-cpu': version_cpu || undefined,\n  'tfjs-backend-webgl': version_webgl || undefined,\n  'tfjs-backend-wasm': version_wasm || undefined,\n};\n// export const version = {};\n"],
+  "mappings": 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   "names": []
 }
diff --git a/package.json b/package.json
index c479a4c1..558d475c 100644
--- a/package.json
+++ b/package.json
@@ -51,20 +51,21 @@
     "tensorflow"
   ],
   "devDependencies": {
-    "@tensorflow/tfjs": "^3.3.0",
-    "@tensorflow/tfjs-backend-cpu": "^3.3.0",
-    "@tensorflow/tfjs-backend-wasm": "^3.3.0",
-    "@tensorflow/tfjs-backend-webgl": "^3.3.0",
-    "@tensorflow/tfjs-converter": "^3.3.0",
-    "@tensorflow/tfjs-core": "^3.3.0",
-    "@tensorflow/tfjs-data": "^3.3.0",
-    "@tensorflow/tfjs-layers": "^3.3.0",
-    "@tensorflow/tfjs-node": "^3.3.0",
-    "@tensorflow/tfjs-node-gpu": "^3.3.0",
+    "@tensorflow/tfjs": "^3.4.0",
+    "@tensorflow/tfjs-backend-cpu": "^3.4.0",
+    "@tensorflow/tfjs-backend-wasm": "^3.4.0",
+    "@tensorflow/tfjs-backend-webgl": "^3.4.0",
+    "@tensorflow/tfjs-converter": "^3.4.0",
+    "@tensorflow/tfjs-core": "^3.4.0",
+    "@tensorflow/tfjs-data": "^3.4.0",
+    "@tensorflow/tfjs-layers": "^3.4.0",
+    "@tensorflow/tfjs-node": "^3.4.0",
+    "@tensorflow/tfjs-node-gpu": "^3.4.0",
     "@types/node": "^14.14.37",
     "@typescript-eslint/eslint-plugin": "^4.22.0",
     "@typescript-eslint/parser": "^4.22.0",
     "@vladmandic/pilogger": "^0.2.16",
+    "canvas": "^2.7.0",
     "chokidar": "^3.5.1",
     "dayjs": "^1.10.4",
     "esbuild": "^0.11.10",
@@ -75,9 +76,10 @@
     "eslint-plugin-node": "^11.1.0",
     "eslint-plugin-promise": "^5.1.0",
     "hint": "^6.1.3",
+    "node-fetch": "^2.6.1",
     "rimraf": "^3.0.2",
     "seedrandom": "^3.0.5",
-    "simple-git": "^2.37.0",
+    "simple-git": "^2.38.0",
     "tslib": "^2.2.0",
     "typedoc": "^0.20.35",
     "typescript": "^4.2.4"
diff --git a/src/blazeface/facepipeline.ts b/src/blazeface/facepipeline.ts
index 4fa7a8a5..cb5f69ae 100644
--- a/src/blazeface/facepipeline.ts
+++ b/src/blazeface/facepipeline.ts
@@ -274,7 +274,7 @@ export class Pipeline {
         angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);
         const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });
         const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
-        const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node
+        const rotatedImage = tf.image.rotateWithOffset(input.toFloat(), angle, 0, faceCenterNormalized); // rotateWithOffset is not defined for tfjs-node
         rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);
         face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshSize, this.meshSize]).div(255);
       }
diff --git a/src/human.ts b/src/human.ts
index e52f2c2f..96b142f1 100644
--- a/src/human.ts
+++ b/src/human.ts
@@ -157,7 +157,7 @@ export class Human {
       faceres: null,
     };
     // export access to image processing
-    // @ts-ignore // typescript cannot infer type
+    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function
     this.image = (input: Input) => image.process(input, this.config);
     // export raw access to underlying models
     this.classes = {
@@ -266,9 +266,7 @@ export class Human {
         this.models.gender,
         this.models.emotion,
         this.models.embedding,
-        // @ts-ignore // typescript cannot infer type
         this.models.handpose,
-        // @ts-ignore // typescript cannot infer type
         this.models.posenet,
         this.models.blazepose,
         this.models.efficientpose,
@@ -280,8 +278,8 @@ export class Human {
         this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),
         this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),
         this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),
-        this.models.handpose || (this.config.hand.enabled ? <Promise<handpose.HandPose>>handpose.load(this.config) : null),
-        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? posenet.load(this.config) : null),
+        this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),
+        this.models.posenet || (this.config.body.enabled && this.config.body.modelPath.includes('posenet') ? <any>posenet.load(this.config) : null),
         this.models.blazepose || (this.config.body.enabled && this.config.body.modelPath.includes('blazepose') ? blazepose.load(this.config) : null),
         this.models.efficientpose || (this.config.body.enabled && this.config.body.modelPath.includes('efficientpose') ? efficientpose.load(this.config) : null),
         this.models.nanodet || (this.config.object.enabled ? nanodet.load(this.config) : null),
@@ -340,7 +338,7 @@ export class Human {
           const simd = await this.tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');
           const mt = await this.tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');
           if (this.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);
-          if (!simd) log('warning: wasm simd support is not enabled');
+          if (this.config.debug && !simd) log('warning: wasm simd support is not enabled');
         }
 
         if (this.config.backend === 'humangl') backend.register();
@@ -574,23 +572,29 @@ export class Human {
 
   /** @hidden */
   #warmupNode = async () => {
-    // @ts-ignore
-    if (typeof tf.node === 'undefined') {
-      if (this.config.debug) log('Warmup tfjs-node not loaded');
-      return null;
-    }
     const atob = (str) => Buffer.from(str, 'base64');
     let img;
     if (this.config.warmup === 'face') img = atob(sample.face);
     if (this.config.warmup === 'body' || this.config.warmup === 'full') img = atob(sample.body);
     if (!img) return null;
-    // @ts-ignore // tf.node is only defined when compiling for nodejs
-    const data = tf.node?.decodeJpeg(img);
-    const expanded = data.expandDims(0);
-    this.tf.dispose(data);
-    // log('Input:', expanded);
-    const res = await this.detect(expanded, this.config);
-    this.tf.dispose(expanded);
+    let res;
+    if (typeof tf['node'] !== 'undefined') {
+      const data = tf['node'].decodeJpeg(img);
+      const expanded = data.expandDims(0);
+      this.tf.dispose(data);
+      // log('Input:', expanded);
+      res = await this.detect(expanded, this.config);
+      this.tf.dispose(expanded);
+    } else {
+      if (this.config.debug) log('Warmup tfjs-node not loaded');
+      /*
+      const input = await canvasJS.loadImage(img);
+      const canvas = canvasJS.createCanvas(input.width, input.height);
+      const ctx = canvas.getContext('2d');
+      ctx.drawImage(img, 0, 0, input.width, input.height);
+      res = await this.detect(input, this.config);
+      */
+    }
     return res;
   }
 
diff --git a/test/README.md b/test/README.md
new file mode 100644
index 00000000..74922714
--- /dev/null
+++ b/test/README.md
@@ -0,0 +1,47 @@
+# Test Results
+
+## Automatic Tests
+
+### NodeJS using TensorFlow library
+
+- Face rotation is disabled for `NodeJS` platform:  
+  `Kernel 'RotateWithOffset' not registered for backend 'tensorflow'`  
+  <https://github.com/tensorflow/tfjs/issues/4606>
+- Image filters are disabled due to lack of Canvas and WeBGL access
+
+### NodeJS with GPU acceleation using CUDA
+
+- Face rotation is disabled for `NodeJS` platform:  
+  `Kernel 'RotateWithOffset' not registered for backend 'tensorflow'`  
+  <https://github.com/tensorflow/tfjs/issues/4606>
+- Image filters are disabled due to lack of Canvas and WeBGL access
+
+### NodeJS using WASM
+
+- Requires dev http server  
+  See <https://github.com/tensorflow/tfjs/issues/4927>
+- Only supported input is Tensor due to missing image decoders
+- Warmup returns null and is marked as failed  
+  Missing native Image implementation in NodeJS
+- Fails on object detection:  
+  `Kernel 'SparseToDense' not registered for backend 'wasm'`  
+  <https://github.com/tensorflow/tfjs/issues/4824>
+
+<br>
+
+## Manual Tests
+
+### Browser using WebGL backend
+
+- Chrome/Edge: All Passing
+- Firefox: WebWorkers not supported due to missing support for OffscreenCanvas
+- Safari: Limited Testing
+
+### Browser using WASM backend
+
+- Chrome/Edge: All Passing
+- Firefox: WebWorkers not supported due to missing support for OffscreenCanvas
+- Safari: Limited Testing
+- Fails on object detection:  
+  `Kernel 'SparseToDense' not registered for backend 'wasm'`  
+  <https://github.com/tensorflow/tfjs/issues/4824>
diff --git a/test/test-main.js b/test/test-main.js
new file mode 100644
index 00000000..c447695a
--- /dev/null
+++ b/test/test-main.js
@@ -0,0 +1,162 @@
+const process = require('process');
+const path = require('path');
+const log = require('@vladmandic/pilogger');
+const canvasJS = require('canvas');
+const fetch = require('node-fetch').default;
+
+let config;
+log.info('test:', path.basename(process.argv[1]));
+
+async function testHTTP() {
+  if (config.modelBasePath.startsWith('file:')) return true;
+  return new Promise((resolve) => {
+    fetch(config.modelBasePath)
+      .then((res) => {
+        if (res && res.ok) log.state('passed: model server:', config.modelBasePath);
+        else log.error('failed: model server:', config.modelBasePath);
+        resolve(res && res.ok);
+      })
+      .catch((err) => {
+        log.error('failed: model server:', err.message);
+        resolve(false);
+      });
+  });
+}
+
+async function getImage(human, input) {
+  let img;
+  try {
+    img = await canvasJS.loadImage(input);
+  } catch (err) {
+    log.error('failed: load image', input, err.message);
+    return img;
+  }
+  const canvas = canvasJS.createCanvas(img.width, img.height);
+  const ctx = canvas.getContext('2d');
+  ctx.drawImage(img, 0, 0, img.width, img.height);
+  const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
+  const res = human.tf.tidy(() => {
+    const tensor = human.tf.tensor(Array.from(imageData.data), [canvas.height, canvas.width, 4], 'int32'); // create rgba image tensor from flat array
+    const channels = human.tf.split(tensor, 4, 2); // split rgba to channels
+    const rgb = human.tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb
+    const reshape = human.tf.reshape(rgb, [1, canvas.width, canvas.height, 3]); // move extra dim from the end of tensor and use it as batch number instead
+    return reshape;
+  });
+  if (res && res.shape[0] === 1 && res.shape[3] === 3) log.state('passed: load image:', input, res.shape);
+  else log.error('failed: load image:', input, res);
+  return res;
+}
+
+function printResults(detect) {
+  const person = (detect.face && detect.face[0]) ? { confidence: detect.face[0].confidence, age: detect.face[0].age, gender: detect.face[0].gender } : {};
+  const object = (detect.object && detect.object[0]) ? { score: detect.object[0].score, class: detect.object[0].label } : {};
+  const body = (detect.body && detect.body[0]) ? { score: detect.body[0].score, keypoints: detect.body[0].keypoints.length } : {};
+  if (detect.face) log.data(' result: face:', detect.face?.length, 'body:', detect.body?.length, 'hand:', detect.hand?.length, 'gesture:', detect.gesture?.length, 'object:', detect.object?.length, person, object, body);
+  if (detect.performance) log.data(' result: performance:', 'load:', detect?.performance.load, 'total:', detect.performance?.total);
+}
+
+async function testInstance(human) {
+  if (human) log.state('passed: create human');
+  else log.error('failed: create human');
+
+  // if (!human.tf) human.tf = tf;
+  log.info('human version:', human.version);
+  log.info('platform:', human.sysinfo.platform, 'agent:', human.sysinfo.agent);
+  log.info('tfjs version:', human.tf.version.tfjs);
+
+  await human.load();
+  if (config.backend === human.tf.getBackend()) log.state('passed: set backend:', config.backend);
+  else log.error('failed: set backend:', config.backend);
+
+  if (human.models) {
+    log.state('passed: load models');
+    const keys = Object.keys(human.models);
+    const loaded = keys.filter((model) => human.models[model]);
+    log.data(' result: defined models:', keys.length, 'loaded models:', loaded.length);
+    return true;
+  }
+  log.error('failed: load models');
+  return false;
+}
+
+async function testWarmup(human, title) {
+  let warmup;
+  try {
+    warmup = await human.warmup(config);
+  } catch (err) {
+    log.error('error warmup');
+  }
+  if (warmup) {
+    log.state('passed: warmup:', config.warmup, title);
+    printResults(warmup);
+    return true;
+  }
+  log.error('failed: warmup:', config.warmup, title);
+  return false;
+}
+
+async function testDetect(human, input, title) {
+  const image = input ? await getImage(human, input) : human.tf.randomNormal([1, 1024, 1024, 3]);
+  if (!image) {
+    log.error('failed: detect: input is null');
+    return false;
+  }
+  let detect;
+  try {
+    detect = await human.detect(image, config);
+  } catch (err) {
+    log.error('error: detect', err);
+  }
+  if (image instanceof human.tf.Tensor) human.tf.dispose(image);
+  if (detect) {
+    log.state('passed: detect:', input || 'random', title);
+    printResults(detect);
+    return true;
+  }
+  log.error('failed: detect', input || 'random', title);
+  return false;
+}
+
+async function test(Human, inputConfig) {
+  config = inputConfig;
+  const ok = await testHTTP();
+  if (!ok) {
+    log.warn('aborting test');
+    return;
+  }
+  const t0 = process.hrtime.bigint();
+  const human = new Human(config);
+  await testInstance(human);
+  config.warmup = 'none';
+  await testWarmup(human, 'default');
+  config.warmup = 'face';
+  await testWarmup(human, 'default');
+  config.warmup = 'body';
+  await testWarmup(human, 'default');
+
+  log.info('test body variants');
+  config.body = { modelPath: 'posenet.json', enabled: true };
+  await testDetect(human, 'assets/human-sample-body.jpg', 'posenet');
+  config.body = { modelPath: 'efficientpose.json', enabled: true };
+  await testDetect(human, 'assets/human-sample-body.jpg', 'efficientpose');
+  config.body = { modelPath: 'blazepose.json', enabled: true };
+  await testDetect(human, 'assets/human-sample-body.jpg', 'blazepose');
+
+  await testDetect(human, null, 'default');
+  log.info('test: first instance');
+  await testDetect(human, 'assets/sample-me.jpg', 'default');
+  log.info('test: second instance');
+  const second = new Human(config);
+  await testDetect(second, 'assets/sample-me.jpg', 'default');
+  log.info('test: concurrent');
+  await Promise.all([
+    testDetect(human, 'assets/human-sample-face.jpg', 'default'),
+    testDetect(second, 'assets/human-sample-face.jpg', 'default'),
+    testDetect(human, 'assets/human-sample-body.jpg', 'default'),
+    testDetect(second, 'assets/human-sample-body.jpg', 'default'),
+  ]);
+  const t1 = process.hrtime.bigint();
+  log.info('test complete:', Math.trunc(parseInt((t1 - t0).toString()) / 1000 / 1000), 'ms');
+}
+
+exports.test = test;
diff --git a/test/test-node-gpu.js b/test/test-node-gpu.js
new file mode 100644
index 00000000..b8bf175f
--- /dev/null
+++ b/test/test-node-gpu.js
@@ -0,0 +1,26 @@
+const Human = require('../dist/human.node-gpu.js').default;
+const test = require('./test-main.js').test;
+
+const config = {
+  modelBasePath: 'file://models/',
+  backend: 'tensorflow',
+  debug: false,
+  videoOptimized: false,
+  async: false,
+  filter: {
+    enabled: true,
+  },
+  face: {
+    enabled: true,
+    detector: { enabled: true, rotation: true },
+    mesh: { enabled: true },
+    iris: { enabled: true },
+    description: { enabled: true },
+    emotion: { enabled: true },
+  },
+  hand: { enabled: true },
+  body: { enabled: true },
+  object: { enabled: true },
+};
+
+test(Human, config);
diff --git a/test/test-node-wasm.js b/test/test-node-wasm.js
new file mode 100644
index 00000000..8400814c
--- /dev/null
+++ b/test/test-node-wasm.js
@@ -0,0 +1,27 @@
+const Human = require('../dist/human.node-wasm.js').default;
+const test = require('./test-main.js').test;
+
+const config = {
+  modelBasePath: 'http://localhost:10030/models/',
+  backend: 'wasm',
+  wasmPath: 'assets/',
+  debug: false,
+  videoOptimized: false,
+  async: false,
+  filter: {
+    enabled: true,
+  },
+  face: {
+    enabled: true,
+    detector: { enabled: true, rotation: true },
+    mesh: { enabled: true },
+    iris: { enabled: true },
+    description: { enabled: true },
+    emotion: { enabled: true },
+  },
+  hand: { enabled: true },
+  body: { enabled: true },
+  object: { enabled: false },
+};
+
+test(Human, config);
diff --git a/test/test-node.js b/test/test-node.js
index 23fd14bc..6fea955a 100644
--- a/test/test-node.js
+++ b/test/test-node.js
@@ -1,100 +1,26 @@
-const log = require('@vladmandic/pilogger');
-// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
-const tf = require('@tensorflow/tfjs-node');
 const Human = require('../dist/human.node.js').default;
+const test = require('./test-main.js').test;
 
 const config = {
+  modelBasePath: 'file://models/',
   backend: 'tensorflow',
   debug: false,
   videoOptimized: false,
   async: false,
-  warmup: 'full',
-  modelBasePath: 'file://models/',
   filter: {
     enabled: true,
   },
   face: {
     enabled: true,
-    detector: { enabled: true, rotation: false },
+    detector: { enabled: true, rotation: true },
     mesh: { enabled: true },
     iris: { enabled: true },
     description: { enabled: true },
     emotion: { enabled: true },
   },
-  hand: {
-    enabled: true,
-  },
-  // body: { modelPath: 'efficientpose.json', enabled: true },
-  // body: { modelPath: 'blazepose.json', enabled: true },
-  body: { modelPath: 'posenet.json', enabled: true },
+  hand: { enabled: true },
+  body: { enabled: true },
   object: { enabled: true },
 };
 
-async function testInstance(human) {
-  if (human) log.state('passed: create human');
-  else log.error('failed: create human');
-
-  // if (!human.tf) human.tf = tf;
-  log.info('human version:', human.version);
-  log.info('tfjs version:', human.tf.version_core);
-  log.info('platform:', human.sysinfo.platform);
-  log.info('agent:', human.sysinfo.agent);
-
-  await human.load();
-  if (human.models) {
-    log.state('passed: load models');
-    const keys = Object.keys(human.models);
-    const loaded = keys.filter((model) => human.models[model]);
-    log.data(' result: defined models:', keys.length, 'loaded models:', loaded.length);
-  } else {
-    log.error('failed: load models');
-  }
-
-  let warmup;
-  try {
-    warmup = await human.warmup();
-  } catch (err) {
-    log.error('error warmup');
-  }
-  if (warmup) {
-    log.state('passed: warmup:', config.warmup);
-    log.data(' result: face:', warmup.face?.length, 'body:', warmup.body?.length, 'hand:', warmup.hand?.length, 'gesture:', warmup.gesture?.length, 'object:', warmup.object?.length);
-    log.data(' result: performance:', 'load:', warmup.performance?.load, 'total:', warmup.performance?.total);
-  } else {
-    log.error('failed: warmup');
-  }
-  const random = tf.randomNormal([1, 1024, 1024, 3]);
-  let detect;
-  try {
-    detect = await human.detect(random);
-  } catch (err) {
-    log.error('error: detect', err);
-  }
-  tf.dispose(random);
-  if (detect) {
-    log.state('passed: detect:', 'random');
-    log.data(' result: face:', detect.face?.length, 'body:', detect.body?.length, 'hand:', detect.hand?.length, 'gesture:', detect.gesture?.length, 'object:', detect.object?.length);
-    log.data(' result: performance:', 'load:', detect?.performance.load, 'total:', detect.performance?.total);
-  } else {
-    log.error('failed: detect');
-  }
-}
-
-async function test() {
-  log.info('testing instance#1 - none');
-  config.warmup = 'none';
-  const human1 = new Human(config);
-  await testInstance(human1);
-
-  log.info('testing instance#2 - face');
-  config.warmup = 'face';
-  const human2 = new Human(config);
-  await testInstance(human2);
-
-  log.info('testing instance#3 - body');
-  config.warmup = 'body';
-  const human3 = new Human(config);
-  await testInstance(human3);
-}
-
-test();
+test(Human, config);
diff --git a/test/test-wasm.js b/test/test-wasm.js
deleted file mode 100644
index f48d74f2..00000000
--- a/test/test-wasm.js
+++ /dev/null
@@ -1,99 +0,0 @@
-const log = require('@vladmandic/pilogger');
-const tf = require('@tensorflow/tfjs');
-const Human = require('../dist/human.node-wasm.js').default;
-
-const config = {
-  backend: 'wasm',
-  wasmPath: 'assets/',
-  debug: false,
-  videoOptimized: false,
-  async: false,
-  modelBasePath: 'http://localhost:10030/models',
-  filter: {
-    enabled: true,
-  },
-  face: {
-    enabled: true,
-    detector: { enabled: true, rotation: false },
-    mesh: { enabled: true },
-    iris: { enabled: true },
-    description: { enabled: true },
-    emotion: { enabled: true },
-  },
-  hand: {
-    enabled: true,
-  },
-  // body: { modelPath: 'efficientpose.json', enabled: true },
-  // body: { modelPath: 'blazepose.json', enabled: true },
-  body: { modelPath: 'posenet.json', enabled: true },
-  object: { enabled: false }, // Error: Kernel 'SparseToDense' not registered for backend 'wasm'
-};
-
-async function testInstance(human) {
-  if (human) log.state('passed: create human');
-  else log.error('failed: create human');
-
-  // if (!human.tf) human.tf = tf;
-  log.info('human version:', human.version);
-  log.info('tfjs version:', human.tf.version_core);
-  log.info('platform:', human.sysinfo.platform);
-  log.info('agent:', human.sysinfo.agent);
-
-  await human.load();
-  if (human.models) {
-    log.state('passed: load models');
-    const keys = Object.keys(human.models);
-    const loaded = keys.filter((model) => human.models[model]);
-    log.data(' result: defined models:', keys.length, 'loaded models:', loaded.length);
-  } else {
-    log.error('failed: load models');
-  }
-
-  let warmup;
-  try {
-    warmup = await human.warmup();
-  } catch (err) {
-    log.error('error warmup');
-  }
-  if (warmup) {
-    log.state('passed: warmup:', config.warmup);
-    log.data(' result: face:', warmup.face?.length, 'body:', warmup.body?.length, 'hand:', warmup.hand?.length, 'gesture:', warmup.gesture?.length, 'object:', warmup.object?.length);
-    log.data(' result: performance:', 'load:', warmup.performance?.load, 'total:', warmup.performance?.total);
-  } else {
-    log.error('failed: warmup');
-  }
-  const random = tf.randomNormal([1, 1024, 1024, 3]);
-  let detect;
-  try {
-    detect = await human.detect(random);
-  } catch (err) {
-    log.error('error: detect', err);
-  }
-  tf.dispose(random);
-  if (detect) {
-    log.state('passed: detect:', 'random');
-    log.data(' result: face:', detect.face?.length, 'body:', detect.body?.length, 'hand:', detect.hand?.length, 'gesture:', detect.gesture?.length, 'object:', detect.object?.length);
-    log.data(' result: performance:', 'load:', detect?.performance.load, 'total:', detect.performance?.total);
-  } else {
-    log.error('failed: detect');
-  }
-}
-
-async function test() {
-  log.info('testing instance#1 - none');
-  config.warmup = 'none';
-  const human1 = new Human(config);
-  await testInstance(human1);
-
-  log.info('testing instance#2 - face');
-  config.warmup = 'face';
-  const human2 = new Human(config);
-  await testInstance(human2);
-
-  log.info('testing instance#3 - body');
-  config.warmup = 'body';
-  const human3 = new Human(config);
-  await testInstance(human3);
-}
-
-test();
diff --git a/wiki b/wiki
index 77b1cd6c..00a239fa 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 77b1cd6cfd86abe0b21aae23e2be2beff84b68ff
+Subproject commit 00a239fa51eda5b366f0e1d05d66fccf4edb0ce1